Using score-informed constraints for NMF-based source separation

Techniques based on non-negative matrix factorization (NMF) have been successfully used to decompose a spectrogram of a music recording into a dictionary of templates and activations. While advanced NMF variants often yield robust signal models, there are usually some inaccuracies in the factorization since the underlying methods are not prepared for phase cancellations that occur when sounds with similar frequency are mixed. In this paper, we present a novel method that takes phase cancellations into account to refine dictionaries learned by NMF-based methods. Our approach exploits the fact that advanced NMF methods are often robust enough to provide information about how sound sources interact in a spectrogram, where they overlap, and thus where phase cancellations could occur. Using this information, the distances used in NMF are weighted entry-wise to attenuate the influence of regions with phase cancellations. Experiments on full-length, polyphonic piano recordings indicate that our method can be successfully used to refine NMF-based dictionaries.

show abstract

“…For our experiment, we used the NMF variant described in [16,17]. In this approach, the dictionary is not randomly initialized.…”

Section: Methodsmentioning

confidence: 99%

Accounting for phase cancellations in non-negative matrix factorization using weighted distances

Ewert

Plumbley

Sandler

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first part of this section presents a comparison of the proposed method by using a glissando signal to the conventional NMF method with harmonic constraints on matrix W [32] and the time-dependent parametric …”

Section: The First Preliminary Test: Glissandomentioning

confidence: 99%

Musical note analysis of solo violin recordings using recursive regularization

Lin

Wang

Chen

et al. 2014

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Composers may not provide instructions for playing their works, especially for instrument solos, and therefore, different musicians may give very different interpretations of the same work. Such differences usually lead to time, amplitude, or frequency variations of musical notes in a phrase in the signal point of view. This paper proposes a frame-based recursive regularization method for time-dependent analysis of each note presenting in solo violin recordings. The system of equations evolves when a new frame is added and an old frame is dropped to track the varying characteristics of violin playing. This method is compared with a time-dependent non-negative matrix factorization method. The complete recordings of both BWV 1005 No. 3 played by Kuijken and 24 Caprices op. 1 no. 24 in A minor played by Paganini are used for the transcription experiment, where the proposed method performs strongly. The analysis results of a short passage extracted from BWV 1005 No. 3 performed by three famous violinists reveal numerous differences in the styles and performances of these violinists.

show abstract

“…One way to utilise the automatically-aligned score is for initialising the pitch activity matrix H in a spectrogram factorisation-based model (see Eq. (3)), and keeping these fixed while the spectral templates W are learned, as in [45]. After the templates are learned, the gain matrix could also be updated in order to cater for note differences between the score and the recording.…”

Section: Score-informed Approachesmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

View full text Add to dashboard Cite

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

show abstract

Using score-informed constraints for NMF-based source separation

Cited by 45 publications

References 4 publications

Accounting for phase cancellations in non-negative matrix factorization using weighted distances

Accounting for phase cancellations in non-negative matrix factorization using weighted distances

Musical note analysis of solo violin recordings using recursive regularization

Automatic music transcription: challenges and future directions

Contact Info

Product

Resources

About