2014/11/25 by Chaitanya Ahuja, Ahuja, Chaitanya, Karan Nathwani +3
Computer Science · #Advanced Data Compression Techniques #Blind Source Separation Techniques #FOS: Computer and information sciences #Sound (cs.SD) #Speech and Audio Processing #cs.SD
paper · pdf · doi:10.48550/arxiv.1411.6741
5 pages, 3 figures
arxiv created 2014/11/25 · openalex publication_date 2014/11/25 · arxiv updated 2014/11/26 · openalex created_date 2025/10/27 · openalex updated_date 2026/07/28
Conventional NMF methods for source separation factorize the matrix of spectral magnitudes. Spectral Phase is not included in the decomposition process of these methods. However, phase of the speech mixture is generally used in reconstructing the target speech signal. This results in undesired traces of interfering sources in the target signal. In this paper the spectral phase is incorporated in the decomposition process itself. Additionally, the complex matrix factorization problem is reduced to an NMF problem using simple transformations. This results in effective separation of speech mixtures since both magnitude and phase are utilized jointly in the separation process. Improvement in source separation results are demonstrated using objective quality evaluations on the GRID corpus.