Patent Grant
10235981
The present application claims the benefit of U.S. application Ser. No. 15/198,499, filed on Jun. 30, 2016, the disclosure of which is hereby incorporated by reference in its entirety.
This invention relates generally to audio mixing techniques and, more specifically, relates to intelligent audio crossfading.
This section is intended to provide a background or context to the invention disclosed below. The description herein may include concepts that could be pursued, but are not necessarily ones that have been previously conceived, implemented or described. Therefore, unless otherwise explicitly indicated herein, what is described in this section is not prior art to the description in this application and is not admitted to be prior art by inclusion in this section.
Crossfading is an audio mixing technique that involves fading a first audio source out while fading a second audio source in at the same time. Simple crossfading does not work well when different types of songs (e.g. different genres, tempo, instrumentation, etc.) are crossfaded. Manual crossfading by DJs can be performed more intelligently, however even in this case crossfading is limited as typical song formats are not separated into instrument tracks. Newer music formats, such as MPEG Spatial Audio Object Coding (SAOC), that deliver partially separated tracks to the consumer. Additionally, newer methods such as blind source separation (BSS) allow instrument tracks to be separated from a mix such as that found in typical music files.
The following summary is merely intended to be exemplary. The summary is not intended to limit the scope of the claims.
In accordance with one aspect, a method includes: separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each instrument track of each of the first plurality and second plurality corresponds to a type of instrument; selecting a first instrument track from the first plurality of instrument tracks and a second instrument track from the second plurality of instrument tracks based at least on the type of instrument corresponding to the first instrument track and the second instrument track; fading out other instrument tracks from the first plurality of instrument tracks; performing a crossfade between the first instrument track and the second instrument track; and fading in other instrument tracks from the second plurality of instrument tracks.
In accordance with another aspect, an apparatus includes at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured, with the at least one processor, to cause the apparatus to perform at least the following: separate a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each of the respective instrument tracks correspond to a type of instrument; select a first instrument track from the first plurality of instrument tracks and a second instrument track from the second plurality of instrument tracks based at least on the type of instrument corresponding to the first instrument track and the second instrument track; crossfade between the first instrument track and the second instrument track; and fade in the other instrument tracks from the second plurality of instrument tracks.
In accordance with another aspect, a computer program product includes a non-transitory computer-readable storage medium having computer program code embodied thereon which when executed by an apparatus causes the apparatus to perform: separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each instrument track of each of the first plurality and second plurality corresponds to a type of instrument; selecting a first instrument track from the first plurality of instrument tracks and a second instrument track from the second plurality of instrument tracks based at least on the type of instrument corresponding to the first instrument track and the second instrument track; fading out other instrument tracks from the first plurality of instrument tracks; performing a crossfade between the first instrument track and the second instrument track; and fading in other instrument tracks from the second plurality of instrument tracks.
The foregoing aspects and other features are explained in the following description, taken in connection with the accompanying drawings, wherein:
The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described in this Detailed Description are exemplary embodiments provided to enable persons skilled in the art to make or use the invention and not to limit the scope of the invention which is defined by the claims.
Referring to
In
The one or more computer readable memories 104 may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as semiconductor based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The computer readable memories 104 may be means for performing storage functions. The processor(s) 101 may be of any type suitable to the local technical environment, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on a multi-core processor architecture, as non-limiting examples. The processor(s) 101 may be means for performing functions, such as controlling the apparatus 100 and other functions as described herein.
In some embodiments, the apparatus 100 may include one or more input and/or output devices 110. The input and/or output devices 110 may be any commonly known device for providing user input to a computer system, e.g. a mouse, a keyboard, a touch pad, a camera, a touch screen, and/or a transducer. The input and/or output devices 110 may also be a commonly known display, projector, or a speaker for providing output to a user.
In general, the various embodiments of the apparatus 100 can include, but are not limited to cellular telephones such as smart phones, tablets, personal digital assistants (PDAs), computers such as desktop and portable computers, gaming devices, music storage and playback appliances, tablets, as well as portable units or terminals that incorporate combinations of such functions. As those skilled in the art will understand, embodiments of the invention are also applicable to music applications and services, such as SPOTIFY, PANDORA, YOUTUBE, and the like.
Embodiments of the invention relate to MPEG Spatial Audio Object Coding (SAOC) where partially separated instrument tracks are delivered to the consumer. MPEG SAOC is described in more detail in the following document 111: Spatial Audio Object Coding. April 2008. <http://mpeg.chiariglione.org/standards/mpeg-d/spatial-audio-object-coding>. MPEG SAOC allows otherwise free editing of the separated instrument tracks, but the resulting audio quality may suffer from too drastic changes. Embodiments also relate to blind sound source separation (BSS), where music instrument tracks can be partially separated from a mix such as that found on a CD for example. BSS separated instrument tracks can also be mixed but they too suffer if the mixing is changed too much as compared to the original, i.e., the separation is partial. With SAOC and BSS the separated tracks are not perfect in the sense that, for example, the separated drum track will contain parts of the other instruments (vocals, guitar, etc.). The drum will dominate the separated drum track but the other instruments are also faintly audible there. SAOC does this separation better than BSS, however, the same problem persists there. If you make the crossfade on the separated drum track, the crossfading may cause problems because the crossfading affects these faintly audible other instruments as well as the drum sound on the separated drum track. For example, if during the crossfading the tempo is drastically changed, this might sound ok with the drum sound but might sound bad with the faintly audible other instruments.
The following documents are relevant to at least some of embodiments described herein: document [2]: Rickard, S. (2007). The DUET Blind Source Separation Algorithm. In S. Makino, T.-W. Lee, & H. Sawada (Eds.), Blind Speech Separation (pp. 217-241). Dordrecht, Netherlands: Springer); document [3]: Eronen, A. (2001, October). Automatic Musical Instrument Recognition, Master of Science Thesis. Tampere, Finland: Tampere University of Technology; document [4]: U.S. Pat. No. 5,952,596 titled Method of changing tempo and pitch of audio by digital signal processing, which is herein incorporated by reference in its entirety; document [5]: S. Nakagawa, “Spoken sentence recognition by time-synchronous parsing algorithm of context-free grammar,” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '87., 1987, pp. 829-832; document [6]: A. P. Klapuri, A. J. Eronen and J. T. Astola, “Analysis of the meter of acoustic musical signals,” in IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 1, pp. 342-355, January 2006; document [7]: Antti Eronen et Al.:NC87157 “Methods for analyzing dominance of tags in music”; document [8]: Peeters, Geoffroy, “Musical Key Estimation of Audio Signal Based on Hidden Markov Modeling of Chroma Vectors”, Proc. of the 9th Int. Conf. on Digital Audio Effects (DAFx-06), Montreal, Canada, Sep. 18-20, 2006, pp. 127-131; and document [9]: Goto, Masataka, Hiroshi G. Okun and Tetsuro Kitahara. “Acoustical-similarity-based Musical Instrument Hierarchy.” Proceedings of the International Symposium on Musical Acoustics, March 31st to April 3rd (2004): 297-300.
In document [2], describes techniques for creating partially separated instrument tracks using BSS from traditional music files. In particular, document [2] provide a DUET Blind Source Separation method which can separate any number of sources using only two mixtures. The method is valid when sources are W-disjoint orthogonal, that is, when the supports of the windowed Fourier transform of the signals in the mixture are disjoint. For anechoic mixtures of attenuated and delayed sources, the method allows one to estimate the mixing parameters by clustering relative attenuation-delay pairs extracted from the ratios of the time-frequency representations of the mixtures. The estimates of the mixing parameters are then used to partition the time-frequency representation of one mixture to recover the original sources. The technique is valid even in the case when the number of sources is larger than the number of mixtures. The method is particularly well suited to speech mixtures because the time-frequency representation of speech is sparse and this leads to W-disjoint orthogonality.
Document [3], describes techniques for recognizing an instrument in each track. Document [3] describes a method where a method which includes pre-processing a signal and transforming the signal into some compact representation that is easier to interpret than the raw waveform. The compact representations may be, for example, LP coefficients, outputs of a mel-filterbank calculated in successive frames, sinusoid envelopes, and a short-time RMSenergy envelope. The method than extracts various characteristic features from the different representations. These representations may contain hundreds or thousands of values calculated at discrete time intervals which are compressed into around 1-50 characteristic features for each note (or for each time interval if using frame-based features). The method then compares the extracted features to a trained model of stored templates to recognize the instrument associated with the signal.
Document [4] provides a method for concurrently changing a tempo and a pitch of an audio signal according to tempo designation information and pitch designation information. An audio signal composed of original amplitude values sequentially is sampled at original sampling points timed by an original sampling rate within an original frame period. The original frame period is converted into an actual frame period by varying a length of the original frame period according to the tempo designation information so as to change the tempo of the audio signal. Each of the original sampling points are converted into each of actual sampling points by shifting each of the original sampling points according to the pitch designation information so as to change the pitch of the audio signal. Each of actual amplitude values are calculated at each of the actual sampling points by interpolating the original amplitude values sampled at original sampling points adjacent to the actual sampling point. The actual amplitude values are sequentially read by the original sampling rate during the actual frame period so as to reproduce a segment of the audio signal within the actual frame period. A series of the segments reproduced by repetition of the actual frame period are smoothly connected to thereby continuously change the tempo and the pitch of the audio signal.
In document [5], describes techniques for recognizing voiced sentences. According to document [5], a method is provided for new continuous speech recognition by phoneme-based word spotting and time-synchronous context-free parsing. The word pattern is composed of the concatenation of phoneme patterns. The knowledge of syntax is given in Backus Normal Form. The method is task-independent in terms of reference patterns and task language. The system first spots word candidates in an input sentence, and then generates a word lattice. The word spotting is performed by a dynamic time warping method. Secondly, the method selects the best word sequences found in the word lattice from all possible sentences which are defined by a context-free grammar.
Document [6] describes techniques for performing musical tempo analysis. According to document [6], a method analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis.
Document [7] describes techniques for recognizing a dominant instrument. Different features are calculated from the audio signal. Best features are first selected before fitting the regression model. This is done by using univariate linear regression tests for the regressors. Best features are used for training a model which predicts the dominance of an instrument. A linear regression model is used to predict the dominance on a scale from 0 to 5. The training is done using a hand-annotated database dominances of instruments for a collection of music tracks.
Document [8] describes a system for the automatic estimation of the key of a music track using hidden Markov models is provided. The front-end of the system performs transient/noise reduction, estimation of the tuning and then represents the track as a succession of chroma vectors over time. The characteristics of the Major and minor modes are learned by training two hidden Markov models on a labeled database. 24 hidden Markov models corresponding to the various keys are then derived from the two trained models. The estimation of the key of a music track is then obtained by computing the likelihood of its chroma sequence given each HMM.
Document [9] describes a method of constructing a musical instrument hierarchy reflecting the similarity of acoustical features. The method uses a feature space and approximates the distribution of each instrument using a large number of sounds. Category-level identification of non-registered instruments is performed using this hierarchy.
According to embodiments described herein, crossfading is performed by separating audio files (e.g. audio files comprising a song, or video files) into individual tracks, such as audio tracks for example, and crossfading between the tracks. The process of separating the files into audio tracks is not always perfect, and frequently an individual instrument track will include sound from other instruments. Thus, the separation is merely ‘partial’ separation. Frequently, the instruments that leak onto an individual instrument track sound similar to individual instrument.
In some example embodiments, crossfading is done using, e.g., information about the dominant instrument of the song. Typically, the dominant instrument separates better than others and thus there are less audible errors from the separation. According to some embodiments, crossfading is performed using a selected instrument that is suitable for the change needed to smoothly change from first song to second song. Similar tracks to the selected instrument track are not faded out during the crossfade because similar tracks tend to leak onto each other and would make the separation errors more audible. According to some embodiments, crossfade is done using based on both the dominant instrument and its similar tracks. These and other embodiments are described in more detail below.
Referring now to
First, at step 202 two songs are analyzed to detect differences between a current song (s1) and a next song (s2), which may be done, e.g., as described in documents [3] and [6]. The difference may include, e.g., a difference in tempo, genre, instrumentation, etc. At step 204, the difference is compared to a threshold value. If the difference is below the threshold value, then a normal crossfade is performed at step 206. If the difference is above the threshold, then the process continues to step 208. At step 208, the two songs are separated into a plurality of audio tracks. In case of traditional audio files, the techniques described in document [3] may be used for example. For MPEG SAOC files, this separation comes automatically based on the file format as each track is its own instrument. At step 210, each of the tracks are analyzed identify at least one instrument on each of the plurality of tracks. Frequently, one instrument will on each track, however, it should be understood that one instrument track may include all percussive instruments (base drum, hi-hat, cymbals etc.), however it should be understood that tracks may include more than one instrument. For example, percussive instruments together considered to be a “single instrument”. The instruments may be identified using, for example, the metadata of a MPEG SAOC file (if such metadata is available) or techniques such as those described in document [4]. At step 212, a dominant instrument is detected for each of the songs. An analysis may be performed on the tracks to determine the dominant instrument by known signal processing means (e.g. as described by document [7]). A dominant instrument typically refers to an instrument that is louder than other instrument tracks on the audio file and/or is more continuously present than other instrument tracks. Separating the dominant instrument track is generally easier than separating other instrument tracks because the other instrument tracks do not leak as much to the separated dominant instrument track. At step 214, all instrument tracks from s1 are faded out except for tracks including the dominant instrument. At step 216, the tempo of dominant instrument tracks of s1 is compared to the tempo of s2. If the tempos are the same, then the process continues to step 220, If the tempos are different, then the tempo of the dominant instrument tracks of s1 are changes to match the tempo of s2 as indicated by step 218. Preferably, the tempo is changed is performed gradually. Additionally, the algorithm that performs the tempo changing may be based on the type of dominant instrument in s1. At step 220, crossfading is performed between the dominant instrument tracks in s1, and the dominant instrument tracks in s2 while keeping the other track (i.e. non-dominant instrument tracks) silenced. Finally, at step 222, the non-dominant instrument tracks in s2 are faded in.
The threshold value in step 204 may be based on the differences between the current song (s1) and the next song (s2). For example, if the difference is a difference in tempo then the threshold value may correspond to beats per minute (e.g. 5 bpm for example). If the difference is genre, changing the whole genre (e.g. from rock to pop) may be considered above a threshold whereas changing between sub-genres may be below the threshold (e.g. classical rock to progressive rock). If the difference is instrumentation changing from a marching band to rock band may be above the threshold value; and the difference may be below threshold value, e.g., when switching from rock band A, which includes one singer, two guitars, one drummer, to rock band B which includes one singer, two guitars, one drummer, one keyboard.
When two songs are crossfaded, if there is a tempo difference or other differences between the songs, some manipulation is needed in order to have the crossfade sound right, which is performed in step 220 above. Typically, the manipulation includes tempo manipulation, musical key manipulation, etc. One factor that should be considered, is that certain manipulation algorithms work best for certain types of instrument tracks. For example, some manipulation algorithms work well for harmonic instruments (e.g. a synthesizer, singing) while other manipulation algorithms are better suited for non-harmonic instruments (e.g. drums). Therefore, a single manipulation algorithm typically does not work well for an entire mix because the mix will generally include many kinds of instruments. A second factor is that manipulation algorithms work best when performed on for single instrument tracks. According to some embodiments, it is preferable to do the manipulation during the crossfade for a separated instrument track based on these two factors. As mentioned above, dominant instruments are generally separated best; therefore, embodiments select a manipulation algorithm based on the type of dominant instrument. This ensures that the selected manipulation algorithm is well suited for the dominant instrument.
Referring now to
Referring now to
Finding similar tracks is described in document [9], for example. For example, similar tracks may found by calculating the cross-correlation between the dominant instrument from the first song and all instruments from the second song and choosing the one with highest correlation. Finding similar tracks may also be performed by, for example, calculating a number of features from the dominant instrument in the first song and from the instruments in the second song and choosing the instrument from the second song that has on average the most similar features. Typical features may include: timbre, tonality, zero-crossing rate, MFCC, LPC, fundamental frequency etc.
Referring now to
In some embodiments the instrument tracks of the first song are faded out and silenced during the duration of the crossfade and one or more of the tracks in of the second song are silenced and faded in after the crossfade, however this is not required. For example, in some embodiments different cross-fading method may be selected for each instrument track such none of tracks are silenced during the fade-in and fade-out. Selecting the crossfading method may be based on the type of instrument. For example, if the instrument tracks to be crossfaded are drums, then a crossfading method optimized for drums may be selected, where if the instrument tracks are synthesizer tracks then a crossfading method optimized for synthesizers may be selected.
It should be understood that the current song (s1) and next song (s2) may have different instruments, and therefore may have different instrument tracks. According to some embodiments, only the instruments that exist in both songs are used for crossfading. For example, if the dominant instrument in the first song is not found in the second song then according to such embodiments the second or third most dominant instrument is used for cross-fading from the first song as long as it is available in the second song. In alternative embodiments, instruments are analyzed for similarity based on different features: tonality, zero-crossing rate, timbre, etc. and the most similar instrument in the second song (compared to the dominant instrument in the first song) is selected. Similarity can be measured by any suitable known method, such as: 1) calculating the cross-correlation between the dominant instrument from the first song and all instruments from the second song and choosing the instrument with highest correlation; or 2) calculating a number of features from the dominant instrument in the first song and from the instruments in the second song and choosing the instrument from the second song that has on average the most similar features. Typical features used in such cases include: timbre, tonality, zero-crossing rate, MFCC, LPC, fundamental frequency etc.
For example, assume s1 includes a piano, and it is determined that the piano is the dominant instrument in s1. Further assume s2 does not include in piano. According to some embodiments, a different instrument in s1 may be selected to perform the crossfade, such that the different instrument is also in s2. Alternatively, a similar instrument may be selected in s2 (e.g. a synthesizer) such that the crossfading is performed between the dominant instrument in s1 and a similar instrument in s2 (e.g. the synthesizer).
In some songs, the dominant instrument track may be a vocal track. If this is the case, additional processing may be required. Vocal tracks are difficult for crossfading because of the possibility of mixing lyrics and because human hearing is sensitive to all changes in speech/vocal signals. If the vocal track in the first song is dominant, according to some embodiments the second most dominant instrument track is selected for crossfading. If there is a clear need for using the vocal track for cross-fading (e.g. user preference or there are no other instrument tracks or the other instruments tracks are relatively much quieter) then a vocal track may be used. In such cases, crossfading of the vocal tracks may be performed, for example, by finding the ends of sentences or places where there are no clear lyrics (e.g. humming, singer is singing ‘ooo-ooo-ooo’ or the like) and the crossfading is performed between sentences or when there are no clear lyrics.
Vocals tracks are difficult because for natural crossfading, and the vocal tracks should be changed after a word or preferably after a sentence. Therefore, the vocal track can be analyzed for word or sentence boundaries (e.g. as describe in document [6]) and fade ins/outs can occur at these boundaries.
According to some embodiments, a first file (e.g. a first song) and a second file (e.g. second song) are separated into tracks as described above, and then it is determined which tracks are vocal tracks. Next, the vocal track of the first song is muted slightly before the end of the song. Slight muting the vocal track is easy as the vocal tracks have been separated from the other tracks. Next, a beat changer is used to change the beat of the first song to match the beat of the second song. According to some embodiments, a beat changer that also causes a pitch change is used. For example, a beat changer using resampling causes a lower pitch and lower tempo if a song is resampled to a higher sampling rate and the original sampling rate is used during play back; whereas a higher pitch and higher tempo is given if resampled to a lower sampling rate and the original sampling rate is used during play back. Beat changers which also cause pitch change work well as they do not cause the other artefacts even with large changes that other beat changers have. The problem with these type of beat changers is that they may sound funny with vocals, e.g., by causing an “Alvin and the chipmunks” effect. However, according to these embodiments the effect is not overly disturbing because the vocal track has been at least partially muted. Typically, gradually changing the beat sounds better than if the beat of the first song is abruptly changed to match the second song. The second song is then faded in with its vocal track muted, and finally the system unmutes the vocal track of the second song.
Non-pitch-changing beat changers have different problems, such as doubled beats, garbled lyrics and other annoying artefacts when the needed change is large for example. Vocals can sound funny and awkward when only the speed changes without changing the pitch. Thus, it can be seen that the typical problems associated with beat changers are reduced using according to these embodiments.
The crossfading described by the embodiments above may be automatically applied to all songs. Alternatively, some embodiments may include an option to detect the genre of two songs, and apply the crossfading based on the genre. For example, crossfading between classical music may not be desired, therefore, in some embodiments the crossfading is not performed when it is determined that the first song and/or the second song is a classical music. This could be determined, for example, based on metadata which provides the genre of the respective songs.
An example use case according to exemplary embodiments is when a user creates a music library with multiple songs (e.g. digital music files) in a music player for example. The music player may identify the dominant instruments in each song, and automatically crossfade across songs based on the identified dominant instruments, which means that some songs may start playing from the first time stamp of such dominant instrument. This type of implementation provides seamless crossfading from one song to another based on selected instruments. In some embodiments, the user may also configure settings in the music player which can decide which instrument to use for such crossfading.
In one example embodiment, a method may include: separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each instrument track of each of the first plurality and second plurality may correspond to a type of instrument as indicated by block 600; selecting a first instrument track from the first plurality of instrument tracks and a second instrument track from the second plurality of instrument tracks based at least on the type of instrument corresponding to the first instrument track and the second instrument track as indicated by block 602; fading out other instrument tracks from the first plurality of instrument tracks as indicated by block 604; performing a crossfade between the first instrument track and the second instrument track as indicated by block 606; and fading in other instrument tracks from the second plurality of instrument tracks as indicated by block 608.
The method may further include: determining a dominant instrument in the first plurality of instrument tracks and a corresponding instrument track in the second plurality of instrument tracks comprising the dominant instrument, wherein the selecting may include: selecting the dominant instrument track as the first instrument track and the corresponding instrument track as the second instrument track. If none of the instrument tracks in the second plurality of instrument tracks comprises the dominant instrument, the method may include at least one of: determining a different dominant instrument in the first plurality of instrument tracks, wherein each of the selected first instrument track and the selected second instrument track may include the different dominant instrument; and determining a similar instrument in the second plurality of instrument tracks as the dominant instrument, wherein the selected first instrument track may comprise the dominant instrument, and the selected second instrument track may comprise the similar instrument. The method may further include: creating a first group of instrument tracks by finding at least one further instrument track in the first plurality that is similar to the selected first instrument track; creating a second group of instrument tracks by finding at least one further instrument track in the second plurality that is similar to the selected second instrument track; and performing the crossfade between the first group of instrument tracks and the second group of instrument tracks. Finding similar instrument tracks may be based on comparing at least one of: loudness, timbre, direction, and zero-crossing rate. The method may further comprise: determining a difference in tempo between the first instrument track and the second instrument track; and adjusting, during the crossfade, the tempo of at least one of the first track and the second track based on the type of instrument. The fading out may include fading out each track in the first plurality of instrument tracks other than the selected first instrument track. The fading in may include fading in each track in the second plurality of instrument tracks other than the selected second instrument track. During at least a portion of the crossfade one or more instrument tracks from the first plurality of instrument tracks that are different from the selected first instrument track may be silenced. During at least a portion of the crossfade one or more instrument tracks from the second plurality of instrument tracks that are different from the selected second instrument track may be silenced. The separation may be based on at least one of: MPEG Spatial Audio Object Coding (SAOC) and blind single sound source separation (BSS).
In one example embodiment, an apparatus (e.g. apparatus 100 of
The at least one memory and the computer program code may be configured, with the at least one processor, to cause the apparatus to: determine a dominant instrument in the first plurality of instrument tracks and a corresponding instrument track in the second plurality of instrument tracks comprising the dominant instrument, wherein the selection may include: selection of the dominant instrument track as the first instrument track and the corresponding instrument track as the second instrument track. If none of the instrument tracks in the second plurality of instrument tracks comprises the dominant instrument, the at least one memory and the computer program code may be configured, with the at least one processor, to cause the apparatus to: determine a different dominant instrument in the first plurality of instrument tracks, wherein each of the selected first instrument track and the selected second instrument track may include the different dominant instrument; and determine a similar instrument in the second plurality of instrument tracks as the dominant instrument, wherein the selected first instrument track may include the dominant instrument, and the selected second instrument track comprises the similar instrument. The at least one memory and the computer program code may be configured, with the at least one processor, to cause the apparatus to perform at least the following: create a first group of instrument tracks by finding at least one further instrument track in the first plurality that is similar to the selected first instrument track; create a second group of instrument tracks by finding at least one further instrument track in the second plurality that is similar to the selected second instrument track; and perform the crossfade between the first group of instrument tracks and the second group of instrument tracks. Finding similar instrument tracks may be based on comparing at least one of: loudness, timbre, direction, and zero-crossing rate. The at least one memory and the computer program code may be configured, with the at least one processor, to cause the apparatus to perform at least the following: determine a difference in tempo between the first instrument track and the second instrument track; and adjust, during the crossfade, the tempo of at least one of the first track and the second track. The adjustment of the tempo may include selecting a tempo manipulation algorithm based on the type of instrument. The fade out may include fading out each track in the first plurality of instrument tracks other than the selected first instrument track. The fade in may include fading in each track in the second plurality of instrument tracks other than the selected second instrument track. The separation may be based on at least one of: MPEG Spatial Audio Object Coding (SAOC) and blind single sound source separation (BSS). The at least one memory and the computer program code may be configured, with the at least one processor, to cause the apparatus to perform at least the following: create a third file comprising the crossfade, and store the third file in the memory for audio playback.
According to another aspect, a computer program product may include a non-transitory computer-readable storage medium having computer program code embodied thereon which when executed by an apparatus may cause the apparatus to perform: separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each of the respective instrument tracks correspond to a type of instrument; selecting a first instrument track from the first plurality of instrument tracks and a second instrument corresponding to the first instrument track and the second instrument track; fading out other instrument tracks from the first plurality of instrument tracks; performing a crossfade between the first instrument track and the second instrument track; and fading in other instrument tracks from the second plurality of instrument tracks.
In one example embodiment, an apparatus may comprise: means for separating a first file into a first plurality of instrument tracks and a second file into a second plurality of instrument tracks, wherein each of the respective instrument tracks correspond to a type of instrument; means for selecting a first instrument track from the first plurality of instrument tracks and a second instrument corresponding to the first instrument track and the second instrument track; means for fading out other instrument tracks from the first plurality of instrument tracks; means for performing a crossfade between the first instrument track and the second instrument track; and means for fading in other instrument tracks from the second plurality of instrument tracks.
Without in any way limiting the scope, interpretation, or application of the claims appearing below, a technical effect of one or more of the example embodiments disclosed herein provide an automated DJ like experience for crossfading between songs.
Embodiments herein may be implemented in software (executed by one or more processors), hardware (e.g., an application specific integrated circuit), or a combination of software and hardware. In an example embodiment, the software (e.g., application logic, an instruction set) is maintained on any one of various conventional computer-readable media. In the context of this document, a “computer-readable medium” may be any media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer, with one example of a computer described and depicted, e.g., in
Any combination of one or more computer readable medium(s) may be utilized as the memory. The computer readable medium may be a computer readable signal medium or a non-transitory computer readable storage medium. A non-transitory computer readable storage medium does not include propagating signals and may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
If desired, the different functions discussed herein may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the above-described functions may be optional or may be combined.
Although various aspects of the invention are set out in the independent claims, other aspects of the invention comprise other combinations of features from the described embodiments and/or the dependent claims with the features of the independent claims, and not solely the combinations explicitly set out in the claims.
It is also noted herein that while the above describes example embodiments of the invention, these descriptions should not be viewed in a limiting sense. Rather, there are several variations and modifications which may be made without departing from the scope of the present invention as defined in the appended claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 3559180 | Joly | Jan 1971 | A |
| 5803747 | Sone | Sep 1998 | A |
| 5952596 | Kondo | Sep 1999 | A |
| 6933432 | Shteyn | Aug 2005 | B2 |
| 7019205 | Fujisawa | Mar 2006 | B1 |
| 7518053 | Jochelson | Apr 2009 | B1 |
| 7732697 | Wieder | Jun 2010 | B1 |
| 7915514 | Shrem | Mar 2011 | B1 |
| 8280539 | Jehan | Oct 2012 | B2 |
| 8319087 | Gossweiler et al. | Nov 2012 | B2 |
| 8487176 | Wieder | Jul 2013 | B1 |
| 8874245 | Reinsch | Oct 2014 | B2 |
| 9070352 | Yang | Jun 2015 | B1 |
| 20020157522 | Cliff | Oct 2002 | A1 |
| 20020172379 | Cliff | Nov 2002 | A1 |
| 20030188625 | Tucmandl | Oct 2003 | A1 |
| 20040254660 | Seefeldt | Dec 2004 | A1 |
| 20090019994 | McKinney | Jan 2009 | A1 |
| 20110011246 | Buskies | Jan 2011 | A1 |
| 20110015767 | Homburg | Jan 2011 | A1 |
| 20120014673 | O'Dwyer | Jan 2012 | A1 |
| 20130290818 | Arrasvuori | Oct 2013 | A1 |
| 20140076125 | Kellett | Mar 2014 | A1 |
| 20140083279 | Little | Mar 2014 | A1 |
| 20140254831 | Patton | Sep 2014 | A1 |
| 20140270181 | Siciliano | Sep 2014 | A1 |
| 20140355789 | Bohrarper | Dec 2014 | A1 |
| 20160086368 | Laaksonen | Mar 2016 | A1 |
| 20160224310 | Wallner | Aug 2016 | A1 |
| 20160372096 | Lyske | Dec 2016 | A1 |
| 20170056772 | Eng | Mar 2017 | A1 |
| 20170098439 | Kojima | Apr 2017 | A1 |
| 20170148425 | Fraga | May 2017 | A1 |
| 20170301372 | Jehan | Oct 2017 | A1 |
| Number | Date | Country |
|---|---|---|
| 2506404 | Apr 2014 | GB |
| 2533654 | Jun 2016 | GB |
| Entry |
|---|
| “Beyond Beatmatching Control the Energy Level (how to DJ)”, http://www.mixedinkey.com/Book/Control-the-Energy-Level-of-Your-DJ-Sets; May 20, 2016, 7 pgs. |
| Spatial Audio Object Coding, http://mpeg.chiariglione.org/standards/mpeg-d/spatial-audio-obiect-coding; Apr. 2008, 2 pgs. |
| Rickard, Scott, “The DUET Blind Source Separation Algorithm”, In S. Makino T.-W. Lee, & H. Sawada (Eds.), Blind Speech Separation, Dordrecht, Netherland: Springer, pp. 217-241, Nov. 2017. |
| Eronen, Antti, “Automatic Musical Instrument Recognition, Master of Science Thesis”, Tampere University of Technology, Oct. 2001, 74 pgs. |
| Nakagawa, Sei-ichi, “Spoken Sentence Recognition by Time-Synchronous Parsing Algorithm of Context-Free Grammar”, IEEE 1987, pp. 829-832. |
| Klapuri, Anssi P., et al., “Analysis of the Meter of Acoustic Musical Signals”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, No. 1, pp. 342-355, Jan. 2006. |
| Peelers, Geoffroy, “Musical Key Estimation of Audio Signal Based on Hidden Markov Modeling of Chroma Vectors”, Proc. Of the 9th Int. Conf. On Digital Audio Effects (DAFx-06), Montreal, Canada, pp. 127-131, Sep. 2006. |
| Goto, Masataka, “Acoustical-similarity-based Musical Instrument Hierarchy and Its Application to Musical Instrument Identification”, Proceedings of the International Symposium on Musical Acoustics, Mar.-Apr. 2004, pp. 297-300. |
| Falch, Cornelia, et al., “Spatial Audio-Object Coding With Enhanced Audio Object Separation”, Proc. of the 13th Int. Conference on Digital Audio Effects (DAFS-10), Sep. 2010, 7 pgs. |
| Number | Date | Country | |
|---|---|---|---|
| 20180277076 A1 | Sep 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15198499 | Jun 2016 | US |
| Child | 15990960 | US |
