Score-Informed Source Separation for Music Signals

In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization. In this contribution, we provide an overview of approaches for score-informed source separation and illustrate their potential by discussing innovative applications and interfaces. Additionally, to illustrate some basic principles behind these approaches, we demonstrate how score information can be integrated into the well-known non-negative matrix factorization (NMF) framework. Finally, we compare this approach to advanced methods based on parametric models

Online score-informed source separation in polyphonic mixtures using instrument spectral patterns

[EN] Soundprism is a real-time algorithm to separate polyphonic music audio into source signals, given the musical score of the audio in advance. This paper presents a framework for a Soundprism implementation. A study of the sound quality of the online score-informed source separation is shown, although a real-time implementation is not carried out. The system is compound of two stages: (1) a score follower that matches a MIDI score position to each time frame of the musical performance; and (2) a source separator based on a nonnegative matrix factorization approach guided by the score. Real audio mixtures composed of an instrumental quartets were employed to obtain preliminary results of the proposed system.Ministerio de Economía y Competitividad. Grant Number: TEC2015-67387-C4-{1, 2, 3}-RMuñoz-Montoro, A.; Vera-Candeas, P.; Cortina, R.; Combarro, EF.; Alonso-Jordá, P. (2019). Online score-informed source separation in polyphonic mixtures using instrument spectral patterns. Computational and Mathematical Methods. 1-10. https://doi.org/10.1002/cmm4.1040S11

Real-time Soundprism

[EN] This paper presents a parallel real-time sound source separation system for decomposing an audio signal captured with a single microphone in so many audio signals as the number of instruments that are really playing. This approach is usually known as Soundprism. The application scenario of the system is for a concert hall in which users, instead of listening to the mixed audio, want to receive the audio of just an instrument, focusing on a particular performance. The challenge is even greater since we are interested in a real-time system on handheld devices, i.e., devices characterized by both low power consumption and mobility. The results presented show that it is possible to obtain real-time results in the tested scenarios using an ARM processor aided by a GPU, when this one is present.This work has been supported by the "Ministerio de Economia y Competitividad" of Spain and FEDER under projects TEC2015-67387-C4-{1,2,3}-R.Muñoz-Montoro, AJ.; Ranilla, J.; Vera-Candeas, P.; Combarro, EF.; Alonso-Jordá, P. (2019). Real-time Soundprism. The Journal of Supercomputing. 75(3):1594-1609. https://doi.org/10.1007/s11227-018-2703-0S15941609753Alonso P, Cortina R, Rodríguez-Serrano FJ, Vera-Candeas P, Alonso-González M, Ranilla J (2017) Parallel online time warping for real-time audio-to-score alignment in multi-core systems. J Supercomput 73:126. https://doi.org/10.1007/s11227-016-1647-5Carabias-Orti JJ, Cobos M, Vera-Candeas P, Rodríguez-Serrano FJ (2013) Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings. 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Audio source separation techniques including novel time-frequency representation tools

The thesis explores the development of tools for audio representation with applications in Audio Source Separation and in the Music Information Retrieval (MIR) field. A novel constant Q transform was introduced, called IIR-CQT. The transform allows a flexible design and achieves low computational cost. Also, an independent development of the Fan Chirp Transform (FChT) with the focus on the representation of simultaneous sources is studied, which has several applications in the analysis of polyphonic music signals. Dierent applications are explored in the MIR field, some of them directly related with the low-level representation tools that were analyzed. One of these applications is the development of a visualization tool based in the FChT that proved to be useful for musicological analysis . The tool has been made available as an open source, freely available software. The proposed Transform has also been used to detect and track fundamental frequencies of harmonic sources in polyphonic music. Also, the information of the slope of the pitch was used to define a similarity measure between two harmonic components that are close in time. This measure helps to use clustering algorithms to track multiple sources in polyphonic music. Additionally, the FChT was used in the context of the Query by Humming application. One of the main limitations of such application is the construction of a search database. In this work, we propose an algorithm to automatically populate the database of an existing Query by Humming, with promising results. Finally, two audio source separation techniques are studied. The first one is the separation of harmonic signals based on the FChT. The second one is an application for which the fundamental frequency of the sources is assumed to be known (Score Informed Source Separation problem)

Score-Informed Source Separation for Musical Audio Recordings [An overview]

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Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years