TY - JOUR
T1 - Sound stream segregation : a neuromorphic approach to solve the “cocktail party problem” in real-time
AU - Thakur, Chetan Singh
AU - Wang, Runchun M.
AU - Afshar, Saeed
AU - Hamilton, Tara J.
AU - Tapson, Jonathan C.
AU - Shamma, Shihab A.
AU - Schaik, André van
PY - 2015
Y1 - 2015
N2 - The human auditory system has the ability to segregate complex auditory scenes into a foreground component and a background, allowing us to listen to specific speech sounds from a mixture of sounds. Selective attention plays a crucial role in this process, colloquially known as the “cocktail party effect.” It has not been possible to build a machine that can emulate this human ability in real-time. Here, we have developed a framework for the implementation of a neuromorphic sound segregation algorithm in a Field Programmable Gate Array(FPGA). This algorithm is based on the principles of temporal coherence and uses an attention signal to separate a target sound stream from background noise. Temporal coherence implies that auditory features belonging to the same sound source are coherently modulated and evoke highly correlated neural response patterns. The basis for this form of sound segregation is that responses from pairs of channels that are strongly positively correlated belong to the same stream while channels that are uncorrelated oranti-correlated belong to different streams. In our framework, we have used an euromorphic cochlea as a front end sound analyser to extract spatial information of the sound input, which then passes through band pass filters that extract the sound envelope at various modulation rates. Further stages include feature extraction and mask generation, which is finally used to reconstruct the targeted sound. Using sample tonal and speech mixtures, we show that our FPGA architecture is able to segregate sound sources in real-time. The accuracy of segregation is indicated by the high signal-to-noise ratio (SNR) of the segregated stream(90, 77, and 55 dB for simple tone, complex tone, and speech, respectively)as compared to the SNR of the mixture wave form(0dB). This system may be easily extended for the segregation of complex speech signals, and may thus find various applications in electronic devices such as for sound segregation and speech recognition.
AB - The human auditory system has the ability to segregate complex auditory scenes into a foreground component and a background, allowing us to listen to specific speech sounds from a mixture of sounds. Selective attention plays a crucial role in this process, colloquially known as the “cocktail party effect.” It has not been possible to build a machine that can emulate this human ability in real-time. Here, we have developed a framework for the implementation of a neuromorphic sound segregation algorithm in a Field Programmable Gate Array(FPGA). This algorithm is based on the principles of temporal coherence and uses an attention signal to separate a target sound stream from background noise. Temporal coherence implies that auditory features belonging to the same sound source are coherently modulated and evoke highly correlated neural response patterns. The basis for this form of sound segregation is that responses from pairs of channels that are strongly positively correlated belong to the same stream while channels that are uncorrelated oranti-correlated belong to different streams. In our framework, we have used an euromorphic cochlea as a front end sound analyser to extract spatial information of the sound input, which then passes through band pass filters that extract the sound envelope at various modulation rates. Further stages include feature extraction and mask generation, which is finally used to reconstruct the targeted sound. Using sample tonal and speech mixtures, we show that our FPGA architecture is able to segregate sound sources in real-time. The accuracy of segregation is indicated by the high signal-to-noise ratio (SNR) of the segregated stream(90, 77, and 55 dB for simple tone, complex tone, and speech, respectively)as compared to the SNR of the mixture wave form(0dB). This system may be easily extended for the segregation of complex speech signals, and may thus find various applications in electronic devices such as for sound segregation and speech recognition.
KW - cochlear
KW - cocktail party effect
KW - neuromorphics
KW - speech perception
UR - http://handle.uws.edu.au:8081/1959.7/uws:32299
U2 - 10.3389/fnins.2015.00309
DO - 10.3389/fnins.2015.00309
M3 - Article
SN - 1662-4548
VL - 9
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 309
ER -