Beta Divergence for Clustering in Monaural Blind Source Separation
نویسندگان
چکیده
General purpose audio blind source separation algorithms have to deal with a large dynamic range for the different sources to be separated. In our algorithm the mixture is separated into single notes. These notes are clustered to construct the melodies played by the active sources. The non-negative matrix factorization (NMF) leads to good results in clustering the notes according to spectral features. The cost function for the NMF is controlled by a parameter beta. The choice of beta depends on the dynamic difference of the sources. The novelty of this paper is to propose a simple classifier to adjust the parameter beta to current dynamic ranges for increasing the separation quality.
منابع مشابه
Source-filter Based Clustering for Monaural Blind Source Separation
In monaural blind audio source separation scenarios, a signal mixture is usually separated into more signals than active sources. Therefore it is necessary to group the separated signals to the final source estimations. Traditionally grouping methods are supervised and thus need a learning step on appropriate training data. In contrast, we discuss unsupervised clustering of the separated channe...
متن کاملMonaural Ica of White Noise Mixtures Is Hard
Separation of monaural linear mixtures of ‘white’ source signals is fundamentally ill-posed. In some situations it is not possible to find the mixing coefficients for the full ‘blind’ problem. If the mixing coefficients are known, the structure of the source prior distribution determines the source reconstruction error. If the prior is strongly multi-modal source reconstruction is possible with...
متن کاملOptimization and Parallelization of Monaural Source Separation Algorithms in the openBliSSART Toolkit
We describe the implementation of monaural audio source separation algorithms in our toolkit openBliSSART (Blind Source Separation for Audio Recognition Tasks). To our knowledge, it provides the first freely available C++ implementation of non-negative matrix factorization (NMF) supporting the Compute Unified Device Architecture (CUDA) for fast parallel processing on graphics processing units (...
متن کاملBlind Source Separation of Monaural Musical Signals Using Complex Wavelets
In this paper, a new method of blind source separation of monaural signals is presented. It is based on similarity criteria between envelopes and frequency trajectories of the components of the signal, and on its onset and offset times. The main difference with previous works is that in this paper, the input signal has been filtered using a flexible complex band pass filter bank that is a discr...
متن کاملRobust Blind Source Separation by Beta Divergence
Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to ou...
متن کامل