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|Title: ||Informed algorithms for sound source separation in enclosed reverberant environments|
|Authors: ||Khan, Muhammad Salman|
|Keywords: ||Sound source separation|
|Issue Date: ||2013|
|Publisher: ||© Muhammad Salman Khan|
|Abstract: ||While humans can separate a sound of interest amidst a cacophony of contending sounds in an echoic environment, machine-based methods lag behind in solving this task. This thesis thus aims at improving performance of audio separation algorithms when they are informed i.e. have access to source location information. These locations are assumed to be known a priori in this work, for example by video processing.
Initially, a multi-microphone array based method combined with binary
time-frequency masking is proposed. A robust least squares frequency invariant data independent beamformer designed with the location information is
utilized to estimate the sources. To further enhance the estimated sources, binary time-frequency masking based post-processing is used but cepstral domain smoothing is required to mitigate musical noise.
To tackle the under-determined case and further improve separation performance
at higher reverberation times, a two-microphone based method
which is inspired by human auditory processing and generates soft time-frequency masks is described. In this approach interaural level difference,
interaural phase difference and mixing vectors are probabilistically modeled in the time-frequency domain and the model parameters are learned
through the expectation-maximization (EM) algorithm. A direction vector is estimated for each source, using the location information, which is used as
the mean parameter of the mixing vector model. Soft time-frequency masks are used to reconstruct the sources. A spatial covariance model is then integrated into the probabilistic model framework that encodes the spatial
characteristics of the enclosure and further improves the separation performance
in challenging scenarios i.e. when sources are in close proximity and
when the level of reverberation is high.
Finally, new dereverberation based pre-processing is proposed based on the cascade of three dereverberation stages where each enhances the twomicrophone
reverberant mixture. The dereverberation stages are based on amplitude spectral subtraction, where the late reverberation is estimated and suppressed. The combination of such dereverberation based pre-processing and use of soft mask separation yields the best separation performance. All methods are evaluated with real and synthetic mixtures formed for example from speech signals from the TIMIT database and measured room impulse responses.|
|Description: ||A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.|
|Appears in Collections:||PhD Theses (Mechanical, Electrical and Manufacturing Engineering)|
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