An Approach to Solving a Permutation Problem of Frequency Domain Independent Component Analysis for Blind Source Separation of Speech Signals
Independent component analysis (ICA) in the
frequency domain is used for solving the problem of blind source
separation (BSS). However, this method has some problems. For
example, a general ICA algorithm cannot determine the permutation
of signals which is important in the frequency domain ICA. In this
paper, we propose an approach to the solution for a permutation
problem. The idea is to effectively combine two conventional
approaches. This approach improves the signal separation
performance by exploiting features of the conventional approaches.
We show the simulation results using artificial data.
Blind source separation, Independent componentanalysis, Frequency domain, Permutation ambiguity.
Enhanced Parallel-Connected Comb Filter Method for Multiple Pitch Estimation
This paper presents an improvement method of
the multiple pitch estimation algorithm using comb filters.
Conventionally the pitch was estimated by using parallel
-connected comb filters method (PCF). However, PCF has
problems which often fail in the pitch estimation when there is
the fundamental frequency of higher tone near harmonics of
lower tone. Therefore the estimation is assigned to a wrong
note when shared frequencies happen. This issue often occurs
in estimating octave 3 or more. Proposed method, for solving
the problem, estimates the pitch with every harmonic instead of
every octave. As a result, our method reaches the accuracy of
more than 80%.
music transcription, pitch estimation, comb filter, fractional delay
An Approach for Blind Source Separation using the Sliding DFT and Time Domain Independent Component Analysis
''Cocktail party problem'' is well known as one of the human auditory abilities. We can recognize the specific sound that we want to listen by this ability even if a lot of undesirable sounds or noises are mixed. Blind source separation (BSS) based on independent component analysis (ICA) is one of the methods by which we can separate only a special signal from their mixed signals with simple hypothesis. In this paper, we propose an online approach for blind source separation using the sliding DFT and the time domain independent component analysis. The proposed method can reduce calculation complexity in comparison with conventional methods, and can be applied to parallel processing by using digital signal processors (DSPs) and so on. We evaluate this method and show its availability.
Cocktail party problem, blind Source Separation(BSS), independent component analysis, sliding DFT, onlineprocessing.