Combining blockwise and multi-coefficient stepwise approches in a general framework for online audio source separation

This article considers the problem of online audio source separation. Various algorithms can be found in the literature, featuring either blockwise or stepwise approaches, and using either the spectral or spatial characteristics of the sound sources of a mixture. We offer an algorithm that can combine both stepwise and blockwise approaches, and that can use spectral and spatial information. We propose a method for pre-processing the data of each block and offer a way to deduce an Equivalent Rectangular Bandwith time-frequency representation out of a Short-Time Fourier Transform. The efficiency of our algorithm is then tested for various parameters and the effect of each of those parameters on the quality of separation and on the computation time is then discussed

TEXT-INFORMED AUDIO SOURCE SEPARATION USING NONNEGATIVE MATRIX PARTIAL CO-FACTORIZATION

We consider a single-channel source separation problem consisting in separating speech from nonstationary background such as music. We introduce a novel approach called text-informed separation, where the source separation process is guided by the corresponding textual information. First, given the text, we propose to produce a speech example via either a speech synthesizer or a human. We then use this example to guide source separation and, for that purpose, we introduce a new variant of the nonnegative matrix partial co-factorization (NMPCF) model based on a so called excitationfilter-channel speech model. The proposed NMPCF model allows sharing the linguistic information between the example speech and the speech in the mixture. We then derive the corresponding multiplicative update (MU) rules for the parameter estimation. Experimental results over different types of mixtures and speech examples show the effectiveness of the proposed approach. Index Terms — Informed audio source separation, text information, nonnegative matrix partial co-factorization, source-filter model 1

Multi-channel audio source separation using multiple deformed references

International audienc

Single channel overlapped-speech detection and separation of spontaneous conversations

PhD ThesisIn the thesis, spontaneous conversation containing both speech mixture and speech dialogue is considered. The speech mixture refers to speakers speaking simultaneously (i.e. the overlapped-speech). The speech dialogue refers to only one speaker is actively speaking and the other is silent. That Input conversation is firstly processed by the overlapped-speech detection. Two output signals are then segregated into dialogue and mixture formats. The dialogue is processed by speaker diarization. Its outputs are the individual speech of each speaker. The mixture is processed by speech separation. Its outputs are independent separated speech signals of the speaker. When the separation input contains only the mixture, blind speech separation approach is used. When the separation is assisted by the outputs of the speaker diarization, it is informed speech separation. The research presents novel: overlapped-speech detection algorithm, and two speech separation algorithms. The proposed overlapped-speech detection is an algorithm to estimate the switching instants of the input. Optimization loop is adapted to adopt the best capsulated audio features and to avoid the worst. The optimization depends on principles of the pattern recognition, and k-means clustering. For of 300 simulated conversations, averages of: False-Alarm Error is 1.9%, Missed-Speech Error is 0.4%, and Overlap-Speaker Error is 1%. Approximately, these errors equal the errors of best recent reliable speaker diarization corpuses. The proposed blind speech separation algorithm consists of four sequential techniques: filter-bank analysis, Non-negative Matrix Factorization (NMF), speaker clustering and filter-bank synthesis. Instead of the required speaker segmentation, effective standard framing is contributed. Average obtained objective tests (SAR, SDR and SIR) of 51 simulated conversations are: 5.06dB, 4.87dB and 12.47dB respectively. For the proposed informed speech separation algorithm, outputs of the speaker diarization are a generated-database. The database associated the speech separation by creating virtual targeted-speech and mixture. The contributed virtual signals are trained to facilitate the separation by homogenising them with the NMF-matrix elements of the real mixture. Contributed masking optimized the resulting speech. Average obtained SAR, SDR and SIR of 341 simulated conversations are 9.55dB, 1.12dB, and 2.97dB respectively. Per the objective tests of the two speech separation algorithms, they are in the mid-range of the well-known NMF-based audio and speech separation methods