APPARATUS, SYSTEMS AND METHODS FOR PROVIDING CLOUD BASED BLIND SOURCE SEPARATION SERVICES

• US 20170178664A1
• Filed: 03/26/2015
• Published: 06/22/2017
• Est. Priority Date: 04/11/2014
• Status: Abandoned Application

• Current Assignee: Analog Devices, Inc.
• Original Assignee: Analog Devices, Inc.
• Inventors: WINGATE, DAVID, VIGODA, BENJAMIN, OHIOMOBA, PATRICK, DONNELLY, BRIAN, STEIN, NOAH DANIEL
• Application Number:
  US15/129,802
• Publication Number:
  US 20170178664A1
• Time in Patent Office: Days
• CPC Class Codes: G10L 15/16 using artificial neural networks
• CPC Class Codes: G10L 2021/02166 Microphone arrays; Beamforming
• CPC Class Codes: G10L 21/0232 Processing in the frequency domain
• CPC Class Codes: G10L 21/028 using properties of sound source
• CPC Class Codes: G10L 21/0308 characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
• CPC Class Codes: G10L 25/30 using neural networks

Abstract

Use of spoken input for user devices, e.g. smartphones, can be challenging due to presence of other sound sources. Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.

Claims

1. A method for processing at least one signal acquired using an acoustic sensor, the at least one signal having contributions from a plurality of acoustic sources, the method comprising using one or more processors performing steps of:

accessing an indication of a current block size, the current block size defining a size of a portion of the at least one signal to be analyzed to separate from the at least one signal one or more contributions from a first acoustic source of the plurality of acoustic sources;

analyzing a first portion of the at least one signal, the first portion being of the current block size, by;

computing one or more first characteristics from data of the first portion, and

using the computed one or more first characteristics, or derivatives thereof, in performing iterations of a nonnegative tensor factorization (NTF) model for the plurality of acoustic sources for the data of the first portion to separate, from at least the first portion of the at least one acquired signal, one or more first contributions from the first acoustic source; and

analyzing a second portion of the at least one signal, the second portion being of the current block size and being temporaly shifted with respect to the first portion, by;

computing one or more second characteristics from data of the second portion, and

using the computed one or more second characteristics, or derivatives thereof, in performing iterations of the NTF model for the data of the second portion to separate, from at least the second portion of the at least one acquired signal, one or more second contributions from the first acoustic source.

2. The method according to claim 1, wherein accessing the indication of the current block size comprises receiving user input providing the indication of the current block size or a derivative thereof.

3. The method according to claim 1, wherein accessing the indication of the current block size comprises computing the current block size based on one or more factors.

4. The method according to claim 1, wherein the first portion and the second portion overlap in time.

143. The method according to claim 1, further comprising applying one or more past statistics computed from data of a past portion of the at least one signal in performing the iterations of the NTF model for the data of the first portion and/or for the data of the second portion,

wherein the past portion comprises a portion of the at least one signal that has been analyzed to separate from the at least one signal one or more contributions from the first acoustic source.

144. The method according to claim 143, wherein:

the past portion comprises a plurality of portions of the at least one signal, each portion of the plurality of portions being of the current block size, and

the one or more past statistics from the data of the past portion comprise a combination of one or more characteristics computed from data of each portion of the plurality of portions and/or results of performing iterations of the NTF model for the data of the each portion.

145. The method according to claim 144, wherein the plurality of portions overlap in time.

146. The method according to claim 1, wherein at least one further signal is acquired using a corresponding further acoustic sensor and wherein analyzing each respective portion of the first portion and the second portion comprises:

computing the one or more characteristics of the respective portion by;

computing respective time-dependent spectral characteristics from the respective portion of the at least one signal, the respective spectral characteristics comprising a plurality of respective components, and

computing respective direction estimates from the at least one signal and the at least one further signal, each component of a first subset of the plurality of respective components having a corresponding one or more of the respective direction estimates, and

using the computed one or more characteristics, or the derivatives thereof, of the respective portion in performing iterations of the NTF model for the data of the respective portion by performing iterations comprising (a) combining respective values of a plurality of parameters of the NTF model with the computed respective direction estimates.

147. The method according to claim 146, wherein performing iterations comprises:

(a) combining the respective values of the plurality of parameters of the NTF model with the computed respective direction estimates to generate, using the NTF model, for each acoustic source of the plurality of acoustic sources, a spectrogram of the acoustic source,

(b) for each acoustic source of the plurality of acoustic sources, scaling a portion of the spectrogram of the acoustic source corresponding to each component of a second subset of the plurality of components by a corresponding scaling factor to generate a scaled spectrogram of the acoustic source, and

(c) updating respective values of at least some of the plurality of parameters based on the scaled spectrograms of the plurality of acoustic sources.

148. The method according to claim 146, wherein the plurality of parameters comprise a direction distribution parameter q(d|s) indicating, for each acoustic source of the plurality of acoustic sources, probability that the acoustic source comprises one or more contributions in each of a plurality of the computed respective direction estimates.

149. The method according to claim 146, further comprising:

combining the computed respective spectral characteristics with the computed respective direction estimates to form a respective data structure representing a distribution indexed by time, frequency, and direction, and

performing the NTF using the formed respective data structure.

5. -73. (canceled)

74. A method for processing at least one signal acquired using a corresponding acoustic sensor, the signal having contributions from a plurality of different acoustic sources, the method comprising using one or more processors performing steps of steps of:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

applying a neural network model to the time-dependent spectral characteristics, the neural network model configured to compute property estimates of a property, each component of a first subset of the components having a corresponding one or more property estimates of the property;

performing iterations of a nonnegative tensor factorization (NTF) model for the plurality of acoustic sources, the iterations comprising (a) combining values of a plurality of parameters of the NTF model with the computed property estimates to separate from the at least one acquired signal one or more contributions from a first acoustic source of the plurality of acoustic sources.

75. The method according to claim 74, wherein performing iterations comprises:

(a) combining values of the plurality of parameters of the NTF model with the computed property estimates to generate, using the NTF model, for each acoustic source of the plurality of acoustic sources, a spectrogram of the acoustic source,

(b) for each acoustic source of the plurality of acoustic sources, scaling a portion of the spectrogram of the acoustic source corresponding to each component of a second subset of the plurality of components by a corresponding scaling factor to generate a scaled spectrogram of the acoustic source, and

(c) updating values of at least some of the plurality of parameters based on the scaled spectrograms of the plurality of acoustic sources.

76. The method according to claim 74, further comprising:

using the values of the plurality of parameters of the NTF model following completion of the iterations to generate a mask for identifying the one or more contributions from the first acoustic source to the time-dependent spectral characteristics; and

applying the generated mask to the time-dependent spectral characteristics to separate the one or more contributions from the first acoustic source.

77. -85. (canceled)

86. A method for processing at least one signal acquired using a corresponding acoustic sensor, the signal having contributions from a plurality of different acoustic sources, the method comprising using one or more processors performing steps of steps of:

computing time-dependent spectral characteristics from the at least one acquired signal, the spectral characteristics comprising a plurality of components;

accessing at least a first model configured to predict contributions from a first acoustic source of the plurality of acoustic sources; and

performing iterations of a nonnegative tensor factorization (NTF) model for the plurality of acoustic sources, the iterations comprising running the first model to separate from the at least one acquired signal one or more contributions from the first acoustic source.

87. The method according to claim 86, wherein performing iterations comprises:

(a) combining values of the plurality of parameters of the NTF model to generate, using the NTF model, for each acoustic source of the plurality of acoustic sources, a spectrogram of the acoustic source;

(b) for each acoustic source of the plurality of acoustic sources, scaling a portion of the spectrogram of the acoustic source corresponding to each component of a first subset of the plurality of components by a corresponding scaling factor to generate a scaled spectrogram of the acoustic source; and

(c) running the first model using at least a portion of the scaled spectrogram as an input to the first model to update values of at least some of the plurality of parameters.

88. The method according to claim 86, further comprising:

using the values of the plurality of parameters of the NTF model following completion of the iterations to generate a mask for identifying the one or more contributions from the first acoustic source to the time-dependent spectral characteristics; and

applying the generated mask to the time-dependent spectral characteristics to separate the one or more contributions from the first acoustic source.

89. -134. (canceled)

135. A system for processing at least one signal acquired using an acoustic sensor, the at least one signal having contributions from a plurality of acoustic sources, the system comprising:

at least one memory configured to store computer executable instructions; and

at least one processor coupled to or comprising the at least one memory and configured, when executing the instructions, to carry out a method comprising;

accessing an indication of a current block size, the current block size defining a size of a portion of the at least one signal to be analyzed to separate from the at least one signal one or more contributions from a first acoustic source of the plurality of acoustic sources;

analyzing a first portion of the at least one signal, the first portion being of the current block size, by;

computing one or more first characteristics from data of the first portion, and

using the computed one or more first characteristics, or derivatives thereof, in performing iterations of a nonnegative tensor factorization (NTF) model for the plurality of acoustic sources for the data of the first portion to separate, from at least the first portion of the at least one acquired signal, one or more first contributions from the first acoustic source; and

analyzing a second portion of the at least one signal, the second portion being of the current block size and being temporaly shifted with respect to the first portion, by;

computing one or more second characteristics from data of the second portion, and

using the computed one or more second characteristics, or derivatives thereof, in performing iterations of the NTF model for the data of the second portion to separate, from at least the second portion of the at least one acquired signal, one or more second contributions from the first acoustic source.

136. The system according to claim 135, further comprising the acoustic sensor.

137. The system according to claim 135, wherein the system is integrated in a client device or in a server, the server communicatively connected to the client device.

138. -142. (canceled)