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A common approach to multiple Direction-of-Arrival (DOA) estimation of speech sources is to identify Time-Frequency (TF) bins with dominant Single Source (SS) and apply DOA estimation such as Multiple Signal Classification (MUSIC) only on those TF bins. In the state-of-the-art Direct Path Dominance (DPD)-MUSIC, the covariance matrix, used as the input to MUSIC, is calculated using only the TF bins...
In this paper, we address the blind source separation (BSS) problem and analyze the optimal window length in the short-time Fourier transform (STFT) for independent low-rank matrix analysis (ILRMA). ILRMA is a state-of-the-art BSS technique that utilizes the statistical independence between low-rank matrix spectrogram models, which are estimated by nonnegative matrix factorization. In conventional...
In this paper, a new Variational Mode Decomposition (VMD) is introduced, and applied to the fundamental frequency estimation of monophonical Turkish maqam music. VMD is a method to decompose an input signal into an ensemble of sub-signals (modes) which is entirely non-recursive. It determines the relevant bands adaptively, and estimates the corresponding modes concurrently. In order to optimally decompose...
Direction of arrival algorithms which exploit the eigenstructure of the spatial covariance matrix (such as MUSIC) encounter difficulties in the presence of strongly correlated sources. Since the broadband polynomial MUSIC is an extension of the narrowband version, it is unsurprising that the same issues arise. In this paper, we extend the spatial smoothing technique to broadband scenarios via spatially...
In this paper, we propose a subspace based method to localize multiple targets in the near field region of a bistatic MIMO system with symmetric uniform linear arrays (ULAs). The proposed method uses the symmetry in the transmitting and receiving arrays to estimate the angle of departure (AOD) and angle of arrival (AOA) of each target by using 1D rank reduction estimator (RARE) based method. For each...
Automatic Music Transcription (AMT) is concerned with the problem of producing the pitch content of a piece of music given a recorded signal. Many methods rely on sparse or low rank models, where the observed magnitude spectra are represented as a linear combination of dictionary atoms corresponding to individual pitches. Some of the most successful approaches use Non-negative Matrix Decomposition...
Music appreciation remains challenging for cochlear implant users. In previous studies a strong negative correlation was found with cochlear implant subjects between music appreciation and music complexity. In this paper, music features that contribute to music complexity are investigated and related to a music preprocessing scheme for cochlear implants, in which a complexity reduction is achieved...
In the last decade, modified subspace DoA estimation methods such as G-MUSIC have been proposed, in the context where the number of available snapshots N is of the same order of magnitude than the number of sensors M. In this context, the conventional MUSIC algorithm fails in presence of close sources because the empirical covariance matrix is a poor estimate of the true covariance matrix. The G-MUSIC...
This paper presents a novel approach to stereophonic music separation based on Non-negative Tensor Factorization (NTF). Stereophonic music is roughly divided into two types; recorded music or synthesized music, which we focus on synthesized one in this paper. Synthesized music signals are often generated as linear combinations of many individual source signals with their mixing gains (i.e., time-invariant...
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