Patent Application
20070291968
The teachings of the present invention can be readily understood by considering the following detailed description in conjunction with the accompanying drawings.
A preferred embodiment of the present invention is now described with reference to the figures where like reference numbers indicate identical or functionally similar elements.
Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
Some portions of the detailed description that follows are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated. It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain arrangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.
However, all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by a variety of operating systems.
The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references below to specific languages are provided for disclosure of enablement and best mode of the present invention.
In addition, the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
The system 200 comprises a mobile platform 210 capable of motions such as rotation, and a set of at least two microphones ML, MR 220. The mobile platform 210 can be, among other platforms, the head of a humanoid robot. The microphones 220 may be mounted on a head 210 of the robot. For the purposes of the following description, but by no means limiting the invention, it is assumed that the microphones ML, MR are placed at opposite sides of the robot head 210 in a symmetrical manner so that when the distances from both microphones 220 to the sound sources are equal, the sound source is right in front of the robot's head 210 (that is, the face of the robot faces the sound source).
The system 200 further comprises a first device L 230 for computing localization cues C based on the signals provided by the microphones ML, MR, 220 and a second device B 250 for transforming cue values C into motor coordinates p using a mapping function T. The location of the sound source can be estimated, for example, using the method disclosed in Kazuhiro Nakadai et al., “Real-time sound source localization and separation for robot audition,” Proceedings of the 2002 international conference on spoken language processing (ICSLP-2002), pp 193-96; and Hiromichi Nakashima et al., “Self-Organization of a Sound Source Localization Robot by Perceptual Cycle,” 9th International Conference on Neural Information Processing (ICONIP'02), 2002, which are incorporated by reference herein in their entirety. These two methods are exemplary, and any other methods may be employed to estimate the location of the sound source using the mapping function.
The system according to one embodiment of the present invention further comprises a learning module S 270 that receives information about current position p and measured cue values C(p). Based on p and C(p), the learning module updates the mapping function T and sends the updated version to the control device B 250. In one embodiment of the invention, the learning module S 270 may also initiate rotating movements of the platform H 210. In another embodiment of the invention, the learning module S 270 may also receive an estimated target position from the second device B 250 for transforming cue values into motor coordinates p using a mapping function T.
The system may also comprise an arbiter device A 260. Nominal target positions for the platform H 210 generated by an external module EXT 270, by the second device B 250 or by the learning module S 270, may all be fed into the arbiter device A 260. The arbiter device A 260 then effects movements on the platform H 210 according to the adaptation method or an external control module.
In step 310, the head of the robot with the microphones mounted are rotated towards the sound source. The rotation may be controlled based on a full scan over all positions performed before the rotation during which IED and/or ITD values at all the positions are measured by the microphones. Alternatively, the rotation may be controlled based on a gradient-based method in which the measured IED/ITD values are analyzed.
In step 320, the Intra-aural Time Difference (ITD) between the two microphones ML, MR is continuously measured by the device L 230 in the position defined by step 310. This is accomplished by reading the next ITD output from the device L 230.
In step 330, it is verified whether the ITD measured in step 320 is smaller than a predefined threshold ε. Because the ITD depends mostly on the differences in distances between the two microphones ML, MR and the sound source, the ITD will approach zero as the difference in the distances between the sound source and the two microphones becomes smaller.
If the ITD equals zero or is close to zero, steps 310 and 320 are reiterated. If the ITD does not equal zero or is close to zero, the algorithm stops, and it is assumed that the microphones ML, MR are aligned so that they are equally distant from the sound source. Rotating the head until the ITD cue value reaches a predefined threshold ε will bring the sound source to a defined relative position vis-à-vis the microphones ML, MR. In case of a robot head with a symmetrical microphone placed on opposite sides of the head, the defined position refers to the position where the face of the robot head faces the sound source.
During the orienting movements of the platform H 210 towards a minimal ITD value, all cue value-position pairs can be stored and later used for adaptation of the mapping once the estimation of the position of the sound source has been terminated.
In one embodiment of the invention, the source signals may be split into different frequency channels (using, for example, FFT or Gamma-tone filter bank as disclosed in Kazuhiro Nakadai et al., “Real-time sound source localization and separation for robot audition,” Proceedings of the 2002 international conference on spoken language processing (ICSLP-2002), pages 193-96). In this case, the best performance may be achieved if the absolute value of ITD summed over many frequency channels (for example, up to approximately 1 kHz) is used.
In another embodiment of the present invention, a method based on IED may be used in addition or alternatively to the above-described method.
In step 610, a first measurement of ITD (and/or IED) is obtained at an initial position p=p1 of the platform H 210. The first measurement is referred to as ITD(p1) in the following description.
In step 620, the platform H is rotated in an arbitrary direction, from an initial position p=p1 to a position p=p2.
In step 630, a second measurement of ITD (and/or IED) is obtained at position p=p2 of the platform H 210. The second measurement is referred to as ITD(p2) in the following description.
In step 640, the true position of the sound source is estimated using the two previously made measurements ITD(p1) and ITD(p2) of the ITD (and/or IED). In one embodiment of the present invention, it is assumed that there is a linear or at least a constant relationship between the location of the sound source and the cue values. Then, the true position s may be estimated using linear interpolation.
Taking two measurements (ITD(p1), ITD(p2)) at two absolute positions p=p1 and p=p2, the true location s of the sound source may be estimated by using the following linear model:
ITD(p)=a*(p−s)
wherein p is the position of the platform H 210, a is the slope of the line, and s the true location of the sound source. Using the two measurements, a and s may be computed as follows:
a=(ITD(pl)−ITD(p2))/(p1−p2)
s=(ITD(p2)*p1−ITD(p1)*p2)/(ITD(p2)−ITD(pl))
Based on this computation, the absolute (body-centered) coordinates p1 and p2 can be translated to relative coordinates (p1-s), (p2-s). The two measurements can also be used for updating the mapping function T. Note that there is no requirement on the values of p1 and p2 other than they be different. Therefore, controlling the orientation of the platform H 210 may be delegated to an external module Ext 270, which allows an adaptation in parallel to the normal operation of the system. After measuring a third position p3 and ITD(p3), the same steps as described above can be performed on p2 and p3.
In another embodiment of the invention, any other functions with a small number of parameters may be used in lieu of a linear model. The precision of the learning mapping depends on the similarity of the model to the true map. In most cases, at least rough mapping can be attained.
Previous methods and systems for sound source localization use a frequency representation from which they extract cues. Therefore, for every sound event there may be a number of cues (e.g., IED, IID, ITD) for many different frequency channels. If it is assumed that only a few channels/cues are decalibrated, it is possible to compute the likely target location from the integration of all cues and channels and then using this estimated position to adapt the mapping.
In a second phase (steps 715, 725, 735, 745), each cue is used individually to estimate the position of the sound source. Then, in step 750, the estimated positions are used to form an average position estimate. The average position estimation may be formed by a method that tolerates errors in individual cues and/or frequency channels. Optionally, the step of computing individual position estimates may be skipped in a method that derives a position estimate directly from the different localization cues.
In another embodiment of the present invention, vision inputs may be integrated at this stage to form an additional cue.
In yet another embodiment of the present invention, different cues and channels in the integration and localization process may be weighted, giving less weigh to cues/channels with low confidence. The computation of confidence may be based on how well a specific cue/channel coincides in its responses (measured over longer periods of time) with the sum of all cues.
A change in the system's gain factor at just one side (or different changes on both sides) can seriously affect the IID cues because the IID is strongly affected by signal gain factors (a factor that linearly scales the signal) of microphones, A/D converters, and software pre-processing. In one embodiment of the present invention, an artificial gain factor g(t) for one microphone (e.g., the left microphone) may be added to resolve this issue. The artificial gain factor g(t) for the microphone can be added by using the following method: Whenever the system is focusing on the sound source (ITD<ε), the additional gain factor g(t) may be updated to bring IID to 0:
g(t+l)=g(t)*(1−alpha2*IID(t|ITD<ε)),
where alpha2 is a learning parameter. This assumes that IID is positive (>0) if the recorded signal is louder in the left microphone than in the right microphone. In the opposite case, the minus sign in the previous equation is replaced by a plus sign. The adaptation of the gain factor may also be done separately for all frequency channels.
The different embodiments described above with reference to
In another embodiment of the invention, now described in relation to
As shown in
If ITD is larger than a predefined threshold, then the method proceeds to step 840, where it is checked whether the overall system has the capacity to execute extra orienting movements or whether it is busy. If the system has the capacity, a new rotation is initiated and the method returns to step 820. If the system is too busy to execute a rotation, then it is checked in step 850 whether the smoothed localization error is high. If the smoothed localization error is high, the method returns to step 840. If the smoothed localization error is not high, then the location of the sound source may be estimated using the methods described above with reference to
The smoothed localization error is computed by measuring the absolute values of the ITD directly after a targeting motion (where ITD values should be around zero). This error is integrated over a plurality of subsequent targeting motions (temporal smoothing).
Learning according to the embodiment of
The only requirement is that another movement be executed. This can be driven by a signal from the module EXT 270 or a target orienting movement.
If the localization error gets too high or can not be reduced any more, the learning approaches described above with reference to
Switching is executed through a cooperation of modules A and S.
The system is adapted in step 870.
Whenever there is nothing else to perform (no outputs from EXT 270 or B 250), the system can start to home in on the sound source (refer to
The system or method according to the present invention, the performance of the localization will be far more robust over longer periods of time. Furthermore, the system or method according to the present invention may result in better cues and/or localization estimation mapping than standard calibration procedures.
While particular embodiments and applications of the present invention have been illustrated and described herein, it is to be understood that the invention is not limited to the precise construction and components disclosed herein and that various modifications, changes, and variations may be made in the arrangement, operation, and details of the methods and apparatuses of the present invention without departing from the spirit and scope of the invention as it is defined in the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 06 011 270 | May 2006 | EP | regional |
