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. EURASIP J Adv Signal Process 2013:184. https://doi.org/10.1186/1687-6180-2013-184Carabias-Orti JJ, Rodriguez-Serrano FJ, Vera-Candeas P, Canadas-Quesada FJ, Ruiz-Reyes N (2015) An audio to score alignment framework using spectral factorization and dynamic time warping. In: 16th International Society for Music Information Retrieval Conference, pp 742–748Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Alonso P, Ranilla J (2014) NNMFPACK: a versatile approach to an NNMF parallel library. In: Proceedings of the 2014 International Conference on Computational and Mathematical Methods in Science and Engineering, pp 456–465Díaz-Gracia N, Cocaña-Fernández A, Alonso-González M, Martínez-Zaldívar FJ, Cortina R, García-Mollá VM, Vidal AM (2015) Improving NNMFPACK with heterogeneous and efficient kernels for $$\beta$$ β -divergence metrics. J Supercomput 71:1846–1856. https://doi.org/10.1007/s11227-014-1363-yDriedger J, Grohganz H, Prätzlich T, Ewert S, Müller M (2013) Score-informed audio decomposition and applications. In: Proceedings of the 21st ACM International Conference on Multimedia, pp 541–544Duan Z, Pardo B (2011) Soundprism: an online system for score-informed source separation of music audio. IEEE J Sel Top Signal Process 5(6):1205–1215Duong NQ, Vincent E, Gribonval R (2010) Under-determined reverberant audio source separation using a full-rank spatial covariance model. IEEE Trans Audio Speech 18(7):1830–1840. https://doi.org/10.1109/TASL.2010.2050716Ewert S, Müller M (2011) Estimating note intensities in music recordings. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp 385–388Ewert S, Pardo B, Mueller M, Plumbley MD (2014) Score-informed source separation for musical audio recordings: an overview. IEEE Signal Process Mag 31:116–124. https://doi.org/10.1109/MSP.2013.2296076Fastl H, Zwicker E (2007) Psychoacoustics. Springer, BerlinGanseman J, Scheunders P, Mysore GJ, Abel JS (2010) Source separation by score synthesis. Int Comput Music Conf 2010:1–4Goto M, Hashiguchi H, Nishimura T, Oka R (2002) RWC music database: popular, classical and jazz music databases. In: ISMIR, vol 2, pp 287–288Goto M (2004) Development of the RWC music database. In: Proceedings of the 18th International Congress on Acoustics (ICA 2004), ppp 553–556Hennequin R, David B, Badeau R (2011) Score informed audio source separation using a parametric model of non-negative spectrogram. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp 45–48. https://doi.org/10.1109/ICASSP.2011.5946324Itoyama K, Goto M, Komatani K et al (2008) Instrument equalizer for query-by-example retrieval: improving sound source separation based on integrated harmonic and inharmonic models. In: ISMIR. https://doi.org/10.1136/bmj.324.7341.827Marxer R, Janer J, Bonada J (2012) Low-latency instrument separation in polyphonic audio using timbre models. In: International Conference on Latent Variable Analysis and Signal Separation, pp 314–321Miron M, Carabias-Orti JJ, Janer J (2015) Improving score-informed source separation for classical music through note refinement. In: ISMIR, pp 448–454Ozerov A, Févotte C (2010) Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Trans Audio Speech Lang Process 18:550–563. https://doi.org/10.1109/TASL.2009.2031510Ozerov A, Vincent E, Bimbot F (2012) A general flexible framework for the handling of prior information in audio source separation. IEEE Trans Audio Speech Lang Process 20:1118–1133. https://doi.org/10.1109/TASL.2011.2172425Pätynen J, Pulkki V, Lokki T (2008) Anechoic recording system for symphony orchestra. Acta Acust United Acust 94:856–865. https://doi.org/10.3813/AAA.918104Raphael C (2008) A classifier-based approach to score-guided source separation of musical audio. Comput Music J 32:51–59. https://doi.org/10.1162/comj.2008.32.1.51Rodriguez-Serrano FJ, Duan Z, Vera-Candeas P, Pardo B, Carabias-Orti JJ (2015) Online score-informed source separation with adaptive instrument models. J New Music Res 44:83–96. https://doi.org/10.1080/09298215.2014.989174Rodriguez-Serrano FJ, Carabias-Orti JJ, Vera-Candeas P, Martinez-Munoz D (2016) Tempo driven audio-to-score alignment using spectral decomposition and online dynamic time warping. ACM Trans Intell Syst Technol 8:1–20. https://doi.org/10.1145/2926717Sawada H, Araki S, Makino S (2011) Underdetermined convolutive blind source separation via frequency bin-wise clustering and permutation alignment. 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Automatic music transcription: challenges and future directions

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

Parallel Online Time Warping for Real-Time Audio-to-Score Alignment in Multi-core Systems

[EN] The Audio-to-Score framework consists of two separate stages: pre- processing and alignment. The alignment is commonly solved through offline Dynamic Time Warping (DTW), which is a method to find the path over the distortion matrix with the minimum cost to determine the relation between the performance and the musical score times. In this work we propose a par- allel online DTW solution based on a client-server architecture. The current version of the application has been implemented for multi-core architectures (x86, x64 and ARM), thus covering either powerful systems or mobile devices. An extensive experimentation has been conducted in order to validate the software. The experiments also show that our framework allows to achieve a good score alignment within the real-time window by using parallel computing techniques.This work has been partially supported by Spanish Ministry of Science and Innovation and FEDER under Projects TEC2012-38142-C04-01, TEC2012-38142-C04-03, TEC2012-38142-C04-04, TEC2015-67387-C4-1-R, TEC2015-67387-C4-3-R, TEC2015-67387-C4-4-R, the European Union FEDER (CAPAP-H5 network TIN2014-53522-REDT), and the Generalitat Valenciana under Grant PROMETEOII/2014/003.Alonso-Jordá, P.; Cortina, R.; Rodríguez-Serrano, F.; Vera-Candeas, P.; Alonso-González, M.; Ranilla, J. (2017). Parallel Online Time Warping for Real-Time Audio-to-Score Alignment in Multi-core Systems. The Journal of Supercomputing. 73(1):126-138. https://doi.org/10.1007/s11227-016-1647-5S126138731Joder C, Essid S, Richard G (2011) A conditional random field framework for robust and scalable audio-to-score matching. 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In: Proceedings of the international computer music conference (ICMC), pp 129–132Simon I, Morris D, Basu S (2008) MySong: automatic accompaniment generation for vocal melodies. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 725–734Rodriguez-Serrano FJ, Duan Z, Vera-Candeas P, Pardo B, Carabias-Orti JJ (2015) Online score-informed source separation with adaptive instrument models. J New Music Res Lond 44(2):83–96Arzt A, Widmer G, Dixon S (2008) Automatic page turning for musicians via real-time machine listening. In: Proceedings of the 18th European conference on artificial intelligence. IOS Press, Amsterdam, pp 241–245Carabias-Orti JJ, Rodriguez-Serrano FJ, Vera-Candeas P, Canadas-Quesada FJ, Ruiz-Reyes N (2015) An audio to score alignment framework using spectral factorization and dynamic time warping. In: 16th International Society for music information retrieval conference, pp 742–748Rodríguez-Serrano FJ, Menéndez-Canal J, Vidal A, Cañadas-Quesada FJ, Cortina R (2015) A DTW based score following method for score-informed sound source separation. In: Proceedings of the 12th sound and music computing conference 2015 (SMC-15), Ireland, pp 491–496Carabias-Ortí JJ, Rodríguez-Serrano FJ, Vera-Candeas P, Cañadas-Quesada FJ, Ruíz-Reyes N (2013) Constrained non-negative sparse coding using learnt instrument templates for realtime music transcription. Eng Appl Artif Intell 26(7):1671–1680Raphael C (2006) Aligning music audio with symbolic scores using a hybrid graphical model. Mach Learn 65:389–409Schreck-Ensemble (2001–2004) ComParser 1.42. http://home.hku.nl/~pieter.suurmond/SOFT/CMP/doc/cmp.html . Accessed Sept 2015Itakura F (1975) Minimum prediction residual principle applied to speech recognition. 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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

Performance Following: Real-Time Prediction of Musical Sequences Without a Score

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Data-Driven Sound Track Generation

Background music is often used to generate a specific atmosphere or to draw our attention to specific events. For example in movies or computer games it is often the accompanying music that conveys the emotional state of a scene and plays an important role for immersing the viewer or player into the virtual environment. In view of home-made videos, slide shows, and other consumer-generated visual media streams, there is a need for computer-assisted tools that allow users to generate aesthetically appealing music tracks in an easy and intuitive way. In this contribution, we consider a data-driven scenario where the musical raw material is given in form of a database containing a variety of audio recordings. Then, for a given visual media stream, the task consists in identifying, manipulating, overlaying, concatenating, and blending suitable music clips to generate a music stream that satisfies certain constraints imposed by the visual data stream and by user specifications. It is our main goal to give an overview of various content-based music processing and retrieval techniques that become important in data-driven sound track generation. In particular, we sketch a general pipeline that highlights how the various techniques act together and come into play when generating musically plausible transitions between subsequent music clips