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. IEEE Trans Audio Speech Lang Process 19(3):516–527. https://doi.org/10.1109/TASL.2010.2051355Vincent E, Araki S, Theis F et al (2012) The signal separation evaluation campaign (2007–2010): achievements and remaining challenges. Signal Process 92:1928–1936. https://doi.org/10.1016/j.sigpro.2011.10.007Vincent E, Bertin N, Gribonval R, Bimbot F (2014) From blind to guided audio source separation: how models and side information can improve the separation of sound. IEEE Signal Process Mag 31:107–115. https://doi.org/10.1109/MSP.2013.229744

ICAR: endoscopic skull‐base surgery

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A comparison of two different methods for score-informed source separation

We present a new method for score-informed source separation, combining ideas from two previous approaches: one based on paramet- ric modeling of the score which constrains the NMF updating process, the other based on PLCA that uses synthesized scores as prior probability distributions. We experimentally show improved separation results using the BSS EVAL and PEASS toolkits, and discuss strengths and weaknesses compared with the previous PLCA-based approach

A comparison of two different methods for score-informed source separation

We present a new method for score-informed source separation, combining ideas from two previous approaches: one based on paramet- ric modeling of the score which constrains the NMF updating process, the other based on PLCA that uses synthesized scores as prior probability distributions. We experimentally show improved separation results using the BSS EVAL and PEASS toolkits, and discuss strengths and weaknesses compared with the previous PLCA-based approach

Combining internal and external robot models for improved model parameter estimation

Experimental robot identification techniques can principally be divided into two categories, based on the type of models they use : internal or external. Internal models relate the joint torques or forces and the motion of the robot; external models relate the reaction forces and torques on the bedplate and the motion data. This paper describes how internal and external robot models can be combined into one identifiable minimal model. This model allows to combine joint torque/force and reaction torque/force measurements in one parameter estimation scheme. This combined model estimation will yield more accurate parameter estimates, and consequently better actuator torque predictions, which is shown by means of a simulated experiment on an industrial robot (KUKA IR 361). This increased accuracy is quite interesting in view of using advanced control algorithms such as computed torque control. (C) 2000 Academic Press