Musical Instrument Sound Multi-Excitation Model for Non-Negative Spectrogram Factorization

Carabias-Orti, Julio J.; Virtanen, Tuomas; Ruiz-Reyes, N.; Canadas-Quesada, F.

doi:10.1109/jstsp.2011.2159700

Cited by 36 publications

(29 citation statements)

References 27 publications

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“…Several works [16,17,22,[40][41][42][43] proposed to restrict the model in Equation 1 to be harmonic. The harmonicity constraint is particularly useful for the analysis and separation of musical audio signals since, by using this constraint, each basis can define a single fundamental frequency.…”

Section: Nmf Backgroundmentioning

confidence: 99%

“…The nonnegativity of the parameters is widely used in music transcription [12,16,17,22] and source separation [36,44]. Under the nonnegativity restriction, the factorization parameters of Equation 3 can be estimated by minimizing the reconstruction error between the observed x( f , t) and the modeledx( f , t) spectrograms.…”

Section: Augmented Nmf For Parameter Estimationmentioning

confidence: 99%

“…As demonstrated in [22], when appropriate training data are available, it is advantageous to learn the instrumentdependent bases in advance and fix them during the analysis of the signals. In fact, this approach has been shown to perform well when the conditions of the music scene do not differ too much between the training and the test data.…”

Section: Instrument Modelingmentioning

confidence: 99%

“…To overcome this issue, many algorithms constrain the original NMF to obtain musically meaningful patterns, for example, by considering a parametric model. In this context, the spectral patterns can be described by harmonic combs [15][16][17], spectrally and/or temporally localized Gaussians [18,19], or by using a source/filter model [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Carabias-Orti

Cobos

Rodriguez-Serrano

2013

EURASIP J. Adv. Signal Process.

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Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge amount of prior information available in this scenario. In fact, besides the special properties of close-microphone tracks, the knowledge on the number and type of instruments making up the mixture can also be successfully exploited for improved separation performance. In this paper, a nonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a set of instrument models are learnt from a training database and incorporated into a multichannel extension of the NMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple music tracks and comparing the results to other state-of-the-art approaches.

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Section: Nmf Backgroundmentioning

confidence: 99%

Section: Augmented Nmf For Parameter Estimationmentioning

confidence: 99%

Section: Instrument Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Carabias-Orti

Cobos

Rodriguez-Serrano

2013

EURASIP J. Adv. Signal Process.

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“…This approach is computationally inexpensive and has been used successfully in a variety of forms for predominant melody extraction Dressler 2012b) as well as multiple pitch estimation (Dressler 2012a). More recently, probabilistic approaches based on decomposition models such as Non-negative Matrix Factorisation (NMF) have gained more interest, especially within source separation scenarios (Marxer 2013;Durrieu, David, and Richard 2011), but also for music transcription (Benetos and Dixon 2011;Carabias-Orti et al 2011;Smaragdis and Brown 2003).…”

Section: Salience Functionsmentioning

confidence: 99%

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Bosch

Marxer

2016

Journal of New Music Research

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The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterised by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signals, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results.

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Applying Source Separation to Music

Pardo

Duan

Richard

2018

Audio Source Separation and Speech Enhancement

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Musical Instrument Sound Multi-Excitation Model for Non-Negative Spectrogram Factorization

Cited by 36 publications

References 27 publications

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music

Applying Source Separation to Music

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