Bitwise Source Separation on Hashed Spectra: An Efficient Posterior Estimation Scheme Using Partial Rank Order Metrics

This paper proposes an efficient bitwise solution to the single-channel source separation task. Most dictionary-based source separation algorithms rely on iterative update rules during the run time, which becomes computationally costly especially when we employ an overcomplete dictionary and sparse encoding that tend to give better separation results. To avoid such cost we propose a bitwise scheme on hashed spectra that leads to an efficient posterior probability calculation. For each source, the algorithm uses a partial rank order metric to extract robust features that form a binarized dictionary of hashed spectra. Then, for a mixture spectrum, its hash code is compared with each source's hashed dictionary in one pass. This simple voting-based dictionary search allows a fast and iteration-free estimation of ratio masking at each bin of a signal spectrogram. We verify that the proposed BitWise Source Separation (BWSS) algorithm produces sensible source separation results for the single-channel speech denoising task, with 6-8 dB mean SDR. To our knowledge, this is the first dictionary based algorithm for this task that is completely iteration-free in both training and testing

Multiple source direction of arrival estimation using subspace pseudointensity vectors

The recently proposed subspace pseudointensity method for direction of arrival estimation is applied in the context of Tasks 1 and 2 of the LOCATA Challenge using the Eigenmike recordings. Specific implementation details are described and results reported for the development dataset, for which the ground truth source directions are available. For both single and multiple source scenarios, the average absolute error angle is about 9 degrees.Comment: In Proceedings of the LOCATA Challenge Workshop - a satellite event of IWAENC 2018 (arXiv:1811.08482

Interference Unmixing and Estimation Technique for Improvement of Speech Separation Performance

Presence of noise in the speech can sometimes become annoying as it can lead to loss of important data or create misunderstandings between the communications area which can lead to major problems associated to loss of time and money. This thesis focuses to filter out noise form a speech signal which is simulated in Matlab/Octave software while making a comparison between temporal resolution of signal with respect to the spectral resolution of the signal in which the parameters such as the size of window length are varied in order to obtain the best speech separation performance. To get the best spectral and temporal resolution with respect to the window length in order to find out the presence of speech sound in the mixture or how strongly the mixture is dominated by the noisy signal. The reconstructed signal is the original speech sound which was applied at the input. To study the relationship between window-disjoint orthogonality and window length and to get the best separation performance

Demixing of Speech Mixtures and Enhancement of Noisy Speech Using ADRess Algorithm

This paper describes the ability of the Azimuth Discrimination and Resynthesis algorithm (ADRess) to separate multiple speech signals from two mixtures in a simulation environment. ADRess exploits the spatial signature of each of the contributing speech sources to demix the mixtures. Speech sentences taken from the TIMIT database and noise signals from the NOISEX database were mixed synthetically to create pairs of mixtures. ADRess can exploit the spatial signature of noise and speech sources to remove or isolate them from a mixture. To simulate the spatial location of different sources the relative attenuation and phase difference of each source between the two mixtures were manipulated. This was performed for numerous different angles of arrival so as to robustly test the algorithm. Objective measures and promising informal listening test results show the suitability of ADRess for cleaning noisy speech mixtures and document the performance of ADRess for speech mixtures with different numbers of sources

Demixing of Speech Mixtures and Enhancement of Noisy Speech Using ADRess Algorithm

This paper describes the ability of the Azimuth Discrimination and Resynthesis algorithm (ADRess) to separate multiple speech signals from two mixtures in a simulation environment. ADRess exploits the spatial signature of each of the contributing speech sources to demix the mixtures. Speech sentences taken from the TIMIT database and noise signals from the NOISEX database were mixed synthetically to create pairs of mixtures. ADRess can exploit the spatial signature of noise and speech sources to remove or isolate them from a mixture. To simulate the spatial location of different sources the relative attenuation and phase difference of each source between the two mixtures were manipulated. This was performed for numerous different angles of arrival so as to robustly test the algorithm. Objective measures and promising informal listening test results show the suitability of ADRess for cleaning noisy speech mixtures and document the performance of ADRess for speech mixtures with different numbers of sources