Sparse Music Representation With Source-Specific Dictionaries and Its Application to Signal Separation

Cho, Namgook; Kuo, C.-C. Jay

doi:10.1109/tasl.2010.2047810

Cited by 21 publications

(18 citation statements)

References 21 publications

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“…So, for each note there are six note-specific atoms in each instrument-specific dictionary. For comparisons, source-specific dictionaries also are constructed (SSD) [10]. A Gabor atom dictionary is used to extract new atoms.…”

Section: Constructing the Instrument-specific Dictionariesmentioning

confidence: 99%

“…The proposed algorithm is evaluated, comparing to SSD method [10]. The average SDR of test signal representation is shown in Table 1.…”

Section: Representation Of Musical Signalsmentioning

confidence: 99%

“…According to the results, the proposed method demonstrates a better efficiency. Also, in [10], the MP algorithm was used to synthesize note-specific atoms, which is a complicated process. However, in our method there is no need to use that algorithm for making an instrument-specific dictionary, and note-specific atoms are computed with a fast and simple process in the frequency domain.…”

Section: Representation Of Musical Signalsmentioning

confidence: 99%

“…Thus, comparison with source-independent methods is out of the scope of this paper, since this comparison has been done previously and the effectiveness of source-dependent algorithms has been proved in [10]. …”

Section: Representation Of Musical Signalsmentioning

confidence: 99%

“…The sparse representation of music signals using sourcespecific dictionary, has been proposed in [10]. The main purpose in their research is to separate music signals from background signals or speech.…”

Section: Introductionmentioning

confidence: 99%

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Representation of Musical Signals using Instrument-Specific Dictionaries

Azamian¹

2017

IJCA

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A new simple method is proposed to synthesize the instrument-specific dictionaries and its use is examined in the time domain musical signal representation. By investigating the spectrum of musical note signals, it is seen that only a small number of frequency elements are significant in the inherent structure of a musical note, and other elements could be omitted. This sparsity is utilized to synthesize note-specific atoms. Firstly, some basic functions are defined from the long-term spectrum of the note signal, called primary atoms. Then the primary atoms that satisfy some conditions are selected as basic atoms and are incorporated to synthesize note-specific atoms. Some usual signal processing windows also are examined such as Gaussian and Hamming windows to synthesize note-specific atoms. The note-specific atoms of an instrument are integrated in an instrument-specific dictionary. A musical signal is represented by mapping to this dictionary by means of the Matching Pursuit algorithm. The proposed method was evaluated on the RWC musical sound database. The results showed that it improves the quality of signal representation compared to some previous methods.

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Section: Constructing the Instrument-specific Dictionariesmentioning

confidence: 99%

“…The proposed algorithm is evaluated, comparing to SSD method [10]. The average SDR of test signal representation is shown in Table 1.…”

Section: Representation Of Musical Signalsmentioning

confidence: 99%

Section: Representation Of Musical Signalsmentioning

confidence: 99%

Section: Representation Of Musical Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%