PUBLICATIONS
[My website has moved to https://serrjoa.github.io/]
2022
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Self-supervised perceptual audio encoding by mixing discriminative and reconstructive tasks.
S. Pascual, J. Serrà, & J. Pons. Patent Application No. ES-202230230 (Mar 18, 2022). On loss functions and evaluation metrics for music source separation. E. Gusó, J. Pons, S. Pascual, & J. Serrà. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP). In press. [arxiv] [DOI] Assessing algorithmic biases for musical version identification. F. Yesiler, M. Miron, J. Serrà, & E. Gómez. Proc. of the ACM Int. Conf. on Web Search and Data Mining (WSDM), pp. 1284-1290. Feb 2022. [arxiv] [DOI] [data+code] Lognormals, power laws and double power laws in the distribution of frequencies of harmonic codewords from classical music. M. Serra-Peralta, J. Serrà, & A. Corral. Scientific Reports 12, 2615. Feb 2022. [arxiv] [DOI] [code] |
2021
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Audio-based musical version identification: elements and challenges.
F. Yesiler, G. Doras, R.M. Bittner, C. Tralie, & J. Serrà. IEEE Signal Processing Magazine 38(6): 115-136. Nov 2021. [arxiv] [DOI] [web] Adversarial auto-encoding for packet loss concealment. S. Pascual, J. Serrà, & J. Pons. Proc. of the IEEE Workshop on Appl. of Signal Proc. to Audio and Acoustics (WASPAA), pp. 71-75. Oct 2021. [arxiv] [DOI] Universal speech enhancement with generative neural networks. J. Serrà, S. Pascual, & J. Pons. Patent Application No. ES-P202130914 (Sep 29, 2021). Heaps' law and vocabulary richness in the history of classical music harmony. M. Serra-Peralta, J. Serrà, & A. Corral. EPJ Data Science 10: 40. Aug 2021. [arxiv] [DOI] [code] Upsampling layers for audio synthesis. J. Pons, J. Serrà, S. Pascual, G. Cengarle, D. Arteaga, & D. Scaini. Patent Application No. ES-P202130417 (May 7, 2021), US-63/220279 (Jul 9, 2021). On tuning consistent annealed sampling for denoising score matching. J. Serrà, S. Pascual, & J. Pons. Technical report. ArXiv: 2104.03725. Apr 2021. [arxiv] Investigating the efficacy of music version retrieval systems for setlist identification. F. Yesiler, E. Molina, J. Serrà, & E. Gómez. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 541-545. Jun 2021. [arxiv] [DOI] [data+code] Upsampling artifacts in neural audio synthesis. J. Pons, S. Pascual, G. Cengarle, & J. Serrà. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3005-3009. Jun 2021. [arxiv] [DOI] [code] Automatic multitrack mixing with a differentiable mixing console of neural audio effects. C.J. Steinmetz, J. Pons, S. Pascual, & J. Serrà. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 71-75. Jun 2021. [arxiv] [DOI] [samples+scripts] SESQA: semi-supervised learning for speech quality assessment. J. Serrà, J. Pons, & S. Pascual. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 381-385. Jun 2021. [arxiv] [DOI] |
2020
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Real-time packet loss concealment using deep generative networks.
S. Pascual, J. Serrà, & J. Pons. Patent Application No. ES-P202031040 (Oct 15, 2020), US-63/195831 (Jun 2, 2021). Less is more: faster and better music version identification with embedding distillation. F. Yesiler, J. Serrà, & E. Gómez. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR). Oct 2020. [arxiv] [ISMIR] Combining musical features for cover detection. G. Doras, F. Yesiler, J. Serrà, E. Gómez, & G. Peeters. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR). Oct 2020. [zenodo] [ISMIR] Experience: advanced network operations in (un-)connected remote communities. D. Perino, X. Yang, J. Serrà, A. Lutu, & I. Leontiadis. Proc. of the ACM Int. Conf. on Mobile Computing and Networking (MobiCom), num. 1. Sep 2020. [ACM] [DOI] Method for learning an audio quality metric combining labeled and unlabeled data. J. Serrà, J. Pons, & S. Pascual. Patent Application No. ES-P202030605 (Jun 22, 2020), US-63/072787 (Aug 31, 2020), EP2021/066786 (Jun 21, 2021). System for automated multitrack mixing in the waveform domain with a learned differentiable mixing console and controller network. C.J. Steinmetz & J. Serrà. Patent Application No. ES-P202030604 (Jun 22, 2020), US-63/072762 (Aug 31, 2020), EP2021/066206 (Jun 16, 2021). Accurate and scalable version identification using musically-motivated embeddings. F. Yesiler, J. Serrà, & E. Gómez. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 21-25. May 2020. [arxiv] [DOI] [Code+model+eval] Input complexity and out-of-distribution detection with likelihood-based generative models. J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J.F. Núñez, & J. Luque. Proc. of the Int. Conf. on Learning Representations (ICLR). Apr 2020. [arxiv] [OpenReview] [Presentation] |
2019
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Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion.
J. Serrà, S. Pascual, & C. Segura. In Advances in Neural Information Processing Systems (NeurIPS) 32: 6790-6800. Dec 2019. [arXiv] [NeurIPS] [Code] [Examples] Towards generalized speech enhancement with generative adversarial networks. S. Pascual, J. Serrà, & A. Bonafonte. Proc. of the Conf. of the Int. Speech Communication Assoc. (INTERSPEECH), pp. 161-165. Sep 2019. [arXiv] [DOI] [Code] [Samples] Learning problem-agnostic speech representations from multiple self-supervised tasks. S. Pascual, M. Ravanelli, J. Serrà, A. Bonafonte, & Y. Bengio. Proc. of the Conf. of the Int. Speech Communication Assoc. (INTERSPEECH), pp. 1791-1795. Sep 2019. [arXiv] [DOI] [Code+model] Time-domain speech enhancement using generative adversarial networks. S. Pascual, J. Serrà, & A. Bonafonte. Speech Communication 114: 10-21. Sep 2019. [DOI] [Code] [Samples1/Samples2] Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech. S. Pascual, J. Serrà, & A. Bonafonte. Applied Sciences 9(16): 3391. Aug 2019. [DOI] [Code] Training neural audio classifiers with few data. J. Pons, J. Serrà, & X. Serra. Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 16-20. May 2019. [arXiv] [DOI] [Code] |
2018
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When the state of the art is ahead of the state of understanding: unintuitive properties of deep neural networks.
J. Serrà. Métode Science Studies Journal 99: 13-17. Dec 2018. [UV] [DOI] There goes Wally: anonymously sharing your location gives you away. A. Pyrgelis, N. Kourtellis, I. Leontiadis, J. Serrà, & C. Soriente. Proc. of the IEEE Int. Conf. on Big Data (BigData), pp. 1218-1227. Dec 2018. [arXiv] [DOI] Real non-volume preserving voice conversion. S. Pascual, J. Serrà, & A. Bonafonte. LXAI Research Workshop (NeurIPS-LXAI). Dec 2018. [TALP] [LXAI] Self-attention linguistic-acoustic decoder. S. Pascual, A. Bonafonte, & J. Serrà. Proc. of the IberSPEECH Conf., pp. 152-156. Nov 2018. [arXiv] [ISCA] Whispered-to-voiced alaryngeal speech conversion with generative adversarial networks. S. Pascual, A. Bonafonte, J. Serrà, & J.A. Gonzalez. Proc. of the IberSPEECH Conf., pp. 117-121. Nov 2018. [arXiv] [ISCA] [Code] Towards a universal neural network encoder for time series. J. Serrà, S. Pascual, & A. Karatzoglou. Proc. of the Int. Conf. of the Catalan Association for Artificial Intelligence (CCIA), Frontiers in Artificial Intelligence and Applications 308, pp. 120-129. Oct 2018. [arXiv] [IOS] MobInsight: a framework using semantic neighborhood features for localized interpretations of urban mobility. S. Park, J. Serrà, E. Frias-Martinez, & N. Oliver. ACM Trans. on Interactive Intelligent Systems 8(3): 23. Jul 2018. [arXiv] [DOI] [Demo] Overcoming catastrophic forgetting with hard attention to the task. J. Serrà, D. Surís, M. Miron, & A. Karatzoglou. Proc. of the Int. Conf. on Machine Learning (ICML) 80: 4555-4564. Jul 2018. [arXiv] [PMLR] [Code] Empirical evidence on daily cash flow time series and its implications for forecasting. F. Salas-Molina, J.A. Rodríguez-Aguilar, J. Serrà, M. Guillen, & F.J. Martín. Statistics and Operations Research Transactions 42(1): 73-98. Jun 2018. [arXiv] [DOI] [Data] Language and noise transfer in speech enhancement generative adversarial network. S. Pascual, M. Park, J. Serrà, A. Bonafonte, & K.-H. Ahn. Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 5019-5023. Apr 2018. [arXiv] [DOI] Unintuitive properties of deep neural networks. J. Serrà. Proc. of the EC Workshop on Human Behaviour and Machine Intelligence (HUMAINT), pp. 11-12. Mar 2018. [EC] |
2017
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Continual prediction of notification attendance with classical and deep network approaches.
K. Katevas, I. Leontiadis, M. Pielot, & J. Serrà. Technical report. Dec 2017. [arXiv] Beyond interruptibility: predicting opportune moments to engage mobile phone users. M. Pielot, B. Cardoso, K. Katevas, J. Serrà, A. Matic, & N. Oliver. Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(3): 91. Sep 2017. Presented at UbiComp 2017. [pielot] [DOI] Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks. J. Serrà & A. Karatzoglou. Proc. of the ACM Conf. on Recommender Systems (RECSYS), pp. 279-287. Aug 2017. [arXiv] [DOI] SEGAN: speech enhancement generative adversarial network. S. Pascual, A. Bonafonte, & J. Serrà. Proc. of the Conf. of the Int. Speech Communication Assoc. (INTERSPEECH), pp. 3642-3646. Aug 2017. [arXiv] [DOI] [Code] [Examples] Class-based prediction errors to detect hate speech with out-of-vocabulary words. J. Serrà, I. Leontiadis, D. Spathis, G. Stringhini, J. Blackburn, & A. Vakali. Proc. of the Conf. of the Association for Computational Linguistics (ACL), Workshop on Abusive Language Online (ALW), pp. 36-40. Aug 2017. [OpenReview] [ACL] Practical processing of mobile sensor data for continual deep learning predictions. K. Katevas, I. Leontiadis, M. Pielot, & J. Serrà. Proc. of the ACM Int. Conf. on Mobile Systems, Applications and Services (MOBISYS), Workshop on Deep Learning for Mobile Systems and Applications (DeepMobile), pp. 19-24. Jun 2017. [arXiv] [DOI] Compact embedding of binary-coded inputs and outputs using Bloom filters. J. Serrà & A. Karatzoglou. Int. Conf. on Learning Representations (ICLR) Workshop. Apr 2017. [OpenReview] The good, the bad, and the KPIs: how to combine performance metrics to better capture under-performing sectors in mobile networks. I. Leontiadis, J. Serrà, A. Finamore, G. Dimopoulos, & K. Papagiannaki. Proc. of the IEEE Int. Conf. on Data Engineering (ICDE), pp. 297-308. Apr 2017. [IEEE] [DOI] Hot or not? Forecasting cellular network hot spots using sector performance indicators. J. Serrà, I. Leontiadis, A. Karatzoglou, & K. Papagiannaki. Proc. of the IEEE Int. Conf. on Data Engineering (ICDE), pp. 259-270. Apr 2017. [arXiv] [DOI] Empowering cash managers to achieve cost savings by improving predictive accuracy. F. Salas-Molina, F.J. Martín, J.A. Rodríguez-Aguilar, J. Serrà, & J.L. Arcos. International Journal of Forecasting 23(2): 403-415. Apr 2017. [arXiv] [DOI] Performance metrics using KPI combinations to better capture underperforming sectors in mobile networks. I. Leontiadis, J. Serrà, & A. Finamore. Patent EP17382164.6, filed on 31/03/2017. Forecast of cellular network hot spots using sector performance indicators. J. Serrà & I. Leontiadis. Patent EP17382163.8, filed on 31/03/2017. Effect of acoustic conditions on algorithms to detect Parkinson's disease from speech. J.C. Vásquez-Correa, J. Serrà, J.R. Orozco-Arroyave, J.F. Vargas-Bonilla, & E. Nöth. Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 5065-5069. Mar 2017. [IEEE] [DOI] |
2016
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A genetic algorithm to discover flexible motifs with support.
J. Serrà, A. Matic, J.L. Arcos, & A. Karatzoglou. Proc. of the IEEE Int. Conf. on Data Mining (ICDM), Workshop on Spatial and Spatiotemporal Data Mining (SSTDM), pp. 1153-1158. Dec 2016. [arXiv] [DOI] [Code] Time-delayed melody surfaces for raga recognition. S. Gulati, J. Serrà, K.K. Ganguli, S. Senturk, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 751-757. Aug 2016. [MTG] [ISMIR] Ranking and significance of variable-length similarity-based time series motifs. J. Serrà, I. Serra, A. Corral, & J.L. Arcos. Expert Systems with Applications 55: 452-460. Aug 2016. [arXiv] [DOI] [Code] What makes a city vital and safe: Bogotá case study. A. Bogomolov, A. Clavijo, M. De Nadai, R. Lara Molina, B. Lepri, E. Letouzé, N. Oliver, G. Pestre, J. Serrà, N. Shoup, & A. Ramirez Suarez. Proc. of the Annual Bank Conf. on Development Economics (ABCDE): Data and Development Economics, session 2D: Crime, Civil Wars, and Hotspots. Jun 2016. [ABCDE1] [ABCDE2] Phrase-based raga recognition using vector space modeling. S. Gulati, J. Serrà, V. Ishwar, S. Senturk, & X. Serra. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 66-70. Mar 2016. [MTG] [DOI] [Code/Data] Discovering raga motifs by characterizing communities in networks of melodic patterns. S. Gulati, J. Serrà, V. Ishwar, & X. Serra. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 286-290. Mar 2016. [MTG] [DOI] [Code/Data] Particle swarm optimization for time series motif discovery. J. Serrà & J.L. Arcos. Knowledge-Based Systems 92: 127-137. Jan 2016. [arXiv] [DOI] [Code] |
2015
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Improving melodic similarity in Indian art music using culture specific melodic characteristics.
S. Gulati, J. Serrà, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 680-686. Oct 2015. [MTG] [ISMIR] Analysis of the impact of a tag recommendation system in a real-world folksonomy. F. Font, J. Serrà, & X. Serra. ACM Trans. on Intelligent Systems and Technology 7(1): 6. Oct 2015. [IIIA] [DOI] Zipf-like distributions in language and music. I. Moreno, F. Font-Clos, J. Serrà, & A. Corral. Complexitat.cat Workshop. May 2015. [complexitat.cat] An evaluation of methodologies for melodic similarity in audio recordings of Indian art music. S. Gulati, J. Serrà, & X. Serra. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 678-682. Apr 2015. [IIIA] [DOI] |
2014
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Mining melodic patterns in large audio collections of Indian art music.
S. Gulati, J. Serrà, V. Ishwar, & X. Serra. Proc. of the Int. Conf. on Signal Image Technology and Internet Based Systems (SITIS), pp. 264-271. Nov 2014. [IIIA] [DOI] [Code] [Data] Melodic pattern extraction in large collections of music recordings using time series mining techniques. S. Gulati, J. Serrà, V. Ishwar, & X. Serra. Demo Session at the Int. Soc. for Music Information Retrieval Conf. (ISMIR). Oct 2014. [IIIA] [ISMIR] An empirical evaluation of similarity measures for time series classification. J. Serrà & J.L. Arcos. Knowledge-Based Systems 67: 305-314. Sep 2014. [IIIA] [DOI] Landmark detection in Hindustani music melodies. S. Gulati, J. Serrà, K.K. Ganguli, & X. Serra. Proc. of the Int. Computer Music Conf. / Sound and Music Computing Conf. (ICMC/SMC), vol. 2, pp. 1062-1068. Sep 2014. [IIIA] [ICMC/SMC] [Data] Class-based tag recommendation and user-based evaluation in online audio clip sharing. F. Font, J. Serrà, & X. Serra. Knowledge-Based Systems 67: 131-142. Sep 2014. [IIIA] [DOI] Unsupervised music structure annotation by time series structure features and segment similarity. J. Serrà, M. Müller, P. Grosche, & J.L. Arcos. IEEE Trans. on Multimedia, Special Issue on Music Data Mining 16(5): 1229-1240. Aug 2014. [IIIA] [DOI] [Code] Intonation analysis of ragas in Carnatic music. G.K. Koduri, V. Ishwar, J. Serrà, & X. Serra. Journal of New Music Research, Special Issue on Computational Approaches to the Art Music Traditions of India and Turkey 43(1): 72-93. Mar 2014. [IIIA] [DOI] Audio clip classification using social tags and the effect of tag expansion. F. Font, J. Serrà, & X. Serra. Proc. of the AES Int. Conf. on Semantic Audio, paper num. 26. Jan 2014. [IIIA] [AES] |
2013
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Folksonomy-based tag recommendation for collaborative tagging systems.
F. Font, J. Serrà, & X. Serra. Int. Journal on Semantic Web and Information Systems 9(2): 1-30. Nov 2013. [IIIA] [DOI] What can we learn from massive music archives? J. Serrà. Dagstuhl Seminar 13451: Computational Audio Analysis. M. Müller, S. Narayanan, and B. Schuller, eds. Wadern, Germany. Nov 2013. [IIIA] [Dagstuhl] Learning of units and knowledge representation. F. Metze, X. Anguera, S. Ewert, J. Gemmeke, D. Kolossa, E. Mower Provost, B. Schuller, & J. Serrà. Dagstuhl Seminar 13451: Computational Audio Analysis. M. Müller, S. Narayanan, and B. Schuller, eds. Wadern, Germany. Nov 2013. [IIIA] [Dagstuhl] Source separation. C. Uhle, J. Driedger, B. Edler, S. Ewert, F. Graf, G. Kubin, M. Müller, N. Ono, B. Pardo, & J. Serrà. Dagstuhl Seminar 13451: Computational Audio Analysis. M. Müller, S. Narayanan, and B. Schuller, eds. Wadern, Germany. Nov 2013. [IIIA] [Dagstuhl] Towards cover group thumbnailing. P. Grosche, M. Müller, & J. Serrà. Proc. of the ACM Int. Conf. on Multimedia (ACM-MM), pp. 613-616. Oct 2013. [IIIA] [DOI] Sample identification in hip-hop music. J. Van Balen, J. Serrà, & M. Haro. In From Sounds to Music and Emotions, M. Aramaki, M. Barthet, R. Kronland-Martinet, and S. Ystad eds., Lecture Notes in Computer Science, vol. 7900, ch. 5, pp. 301-312. Sep 2013. [IIIA] [DOI] Note onset deviations as musical piece signatures. J. Serrà, T.H. Özaslan, & J.L. Arcos. PLoS ONE 8(7): e69268. Jul 2013. [PLoS] [DOI] Cognitive prognosis of acquired brain injury patients using machine learning techniques. J. Serrà, J.L. Arcos, A. García-Rudolph, A. García-Molina, T. Roig, & J.M. Tormos. Proc. of the Int. Conf. on Advanced Cognitive Technologies and Applications (COGNITIVE), pp. 108-113. May 2013. [IIIA] [CSIC] Measuring quantitative trends in western popular music. J. Serrà, A. Corral, M. Boguñá, M. Haro, & J.L. Arcos. CRM-Imperial College Workshop on Complex Systems. Barcelona, Spain. Apr 2013. [IIIA] [CRM] Tonal representations for music retrieval: from version identification to query-by-humming. J. Salamon, J. Serrà, & E. Gómez. Int. Journal of Multimedia Information Retrieval 2(1): 45-58. Feb 2013. [IIIA] [DOI] |
2012
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Structure-based audio fingerprinting for music retrieval.
P. Grosche, J. Serrà, M. Müller, & J.L. Arcos. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 55-60. Oct 2012. [IIIA] [ISMIR] Folksonomy-based tag recommendation for online audio clip sharing. F. Font, J. Serrà, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 73-78. Oct 2012. [IIIA] [ISMIR] Characterizaztion of intonation in Carnatic music by parametrizing pitch histograms. G.K. Koduri, J. Serrà, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 199-204. Oct 2012. [IIIA] [ISMIR] Extracting semantic information from an on-line Carnatic music forum. M. Sordo, J. Serrà, G.K. Koduri, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 355-360. Oct 2012. [IIIA] [ISMIR] The importance of detecting boundaries in music structure annotation. J. Serrà, M. Müller, P. Grosche, & J.L. Arcos. Music Information Retrieval Evaluation eXchange (MIREX). Oct 2012. [IIIA] [MIREX] A competitive measure to assess the similarity between two time series. J. Serrà & J.L. Arcos. Proc. of the Int. Conf. on Case-Based Reasoning (ICCBR), Lecture Notes in Artificial Intelligence 7466, pp. 414-427. Sep 2012. [IIIA] [DOI] [Code] The computer as music critic. J. Serrà & J.L. Arcos. The New York Times, pp. SR12. September 15, 2012. [IIIA] [NYTimes] Measuring the evolution of contemporary western popular music. J. Serrà, A. Corral, M. Boguñá, M. Haro & J.L. Arcos. Scientific Reports 2: 521. Jul 2012. [IIIA] [DOI] Characterization and exploitation of community structure in cover song networks. J. Serrà, M. Zanin, P. Herrera, & X. Serra. Pattern Recognition Letters 33(9): 1032-1041. Jul 2012. [arXiv] [DOI] Unsupervised detection of music boundaries by time series structure features. J. Serrà, M. Müller, P. Grosche, & J.L. Arcos. Proc. of the AAAI Int. Conf. on Artificial Intelligence (AAAI), pp. 1613-1619. Jul 2012. [IIIA] [AAAI] Extracting semantic information from on-line art music discussion forums. M. Sordo, J. Serrà, G.K. Koduri, & X. Serra. CompMusic Workshop. Jul 2012. [IIIA] [CompMusic] Computational analysis of intonation in Indian art music. G.K. Koduri, J. Serrà, & X. Serra. CompMusic Workshop. Jul 2012. [IIIA] [CompMusic] Automatic identification of samples in hip hop music. J. Van Balen, M. Haro, & J. Serrà. Proc. of the Int. Symp. on Computer Music Modeling and Retrieval (CMMR), pp. 544-551. Jun 2012. [IIIA] [CMMR] Quantifying the evolution of popular music. J. Serrà, A. Corral, M. Boguñá, M. Haro, & J.L. Arcos. No Lineal Conf. Jun 2012. [IIIA] [NoLineal] Patterns, regularities, and evolution of contemporary popular music. J. Serrà, A. Corral, M. Boguñá, M. Haro, & J.L. Arcos. Complexitat.Cat Workshop. May 2012. [IIIA] [complexitat.cat] Power-law distribution in encoded MFCC frames of speech, music, and environmental sound signals. M. Haro, J. Serrà, A. Corral, & P. Herrera. Proc. of the Int. World Wide Web Conf. (WWW), Workshop on Advances in Music Information Research (AdMIRe), pp. 895-902. Apr 2012. [IIIA] [WWW] Melody, bassline, and harmony representations for music version identification. J. Salamon, J. Serrà, & E. Gómez. Proc. of the Int. World Wide Web Conf. (WWW), Workshop on Advances in Music Information Research (AdMIRe), pp. 887-894. Apr 2012. [IIIA] [WWW] Audio content-based music retrieval. P. Grosche, M. Müller, & J. Serrà. In Multimodal Music Processing, M. Müller, M. Goto, and M. Schedl eds., Dagstuhl Follow-Ups, Dagstuhl Publishing, Wadern, Germany, vol. 3, ch. 9, pp. 157-174. Apr 2012. [IIIA] [Dagstuhl] Zipf's law in short-time timbral codings of speech, music, and environmental sound signals. M. Haro, J. Serrà, P. Herrera, & A. Corral. PLoS ONE 7(3): e33993. Mar 2012. [IIIA] [DOI] Predictability of music descriptor time series and its application to cover song detection. J. Serrà, H. Kantz, X. Serra, & R.G. Andrzejak. IEEE Trans. on Audio, Speech and Language Processing 20(2): 514-525. Feb 2012. [MTG] [DOI] |
2011
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Identification of versions of the same musical composition: audio content-based approaches and post-processing steps.
J. Serrà. LAP Lambert Academic Publishing, Saarbrücken, Germany. ISBN 978-3-8473-2785-1. Dec 2011. [Amazon] [BN] Assessing the tuning of sung Indian classical music. J. Serrà, G.K. Koduri, M. Miron, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 263-268. Oct 2011. [MTG] [ISMIR] Computational approaches for the understanding of melody and rhythm in Carnatic music. G.K. Koduri, M. Miron, J. Serrà, & X. Serra. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 157-162. Oct 2011. [MTG] [ISMIR] Unifying low-level and high-level music similarity measures. D. Bogdanov, J. Serrà, N. Wack, P. Herrera, & X. Serra. IEEE Trans. on Multimedia 13(4): 687-701. Aug 2011. [MTG] [DOI] Method for calculating measures of similarity between time signals. J. Serrà. Patent US 2011/0178615, published July 21, 2011. Priority num. ES20090001057-20090423. Also published as ES 2354330 (Método para calcular medidas de similitud entre señales temporales). [FreePatentsOnline] [EspaceNet] Nonlinear audio recurrence analysis with application to genre classification. J. Serrà, C.A. De Los Santos, & R.G. Andrzejak. Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 169-172. May 2011. [MTG] [DOI] Identification of versions of the same musical composition by processing audio descriptions. J. Serrà. PhD Thesis. Universitat Pompeu Fabra, Barcelona, Spain. Mar 2011. [MTG] [TDX] Cover song networks: analysis and accuracy increase. J. Serrà, M. Zanin, & P. Herrera. Int. Journal of Complex Systems in Science 1: 55-59. Jan 2011. [MTG] |
2010 & Before...
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Model-based cover song detection via threshold autoregressive forecasts.
J. Serrà, H. Kantz, & R.G. Andrzejak. Proc. of the ACM Int. Conf. on Multimedia (ACM-MM), Workshop on Music and Machine Learning (MML), pp. 13-16. Oct 2010. [MTG] [DOI] Unsupervised accuracy improvement for cover song detection using spectral connectivity network. M. Lagrange & J. Serrà. Proc. of the Int. Soc. for Music Information Retrieval Conf. (ISMIR), pp. 595-600. Aug 2010. [MTG] [ISMIR] Hybrid music similarity measure. D. Bogdanov, J. Serrà, N. Wack, & P. Herrera. Music Information Retrieval Evaluation eXchange (MIREX). Aug 2010. [MTG] [MIREX] Music classification using high-level models. N. Wack, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, E. Gómez, & P. Herrera. Music Information Retrieval Evaluation eXchange (MIREX). Aug 2010. [MTG] [MIREX] Cover song networks: analysis and accuracy increase. J. Serrà, M. Zanin, & P. Herrera. Net-Works Int. Conf. Jun 2010. [MTG] [Net-Works] Indexing music by mood: design and integration of an automatic content-based annotator. C. 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Music Information Retrieval Evaluation eXchange (MIREX). Oct 2009. [MTG] [MIREX] Assessing the results of a cover song identification system with coverSSSSearch. J. Serrà. Demo Session at the Int. Soc. for Music Information Retrieval Conf. (ISMIR). Oct 2009. [MTG] Cross recurrence quantification for cover song identification. J. Serrà, X. Serra, & R.G. Andrzejak. New Journal of Physics 11: 093017. Sep 2009. [MTG] [DOI] [Code] Shape-based spectral contrast descriptor. V. Akkermans, J. Serrà, & P. Herrera. Proc. of the Sound and Music Computing Conf. (SMC), pp. 143-148. Jul 2009. [MTG] [SMC] Music mood annotator design and integration. C. Laurier, O. Meyers, J. Serrà, M. Blech, & P. Herrera. Proc. of the Int. Workshop on Content-Based Multimedia Indexing (CBMI), pp. 156-161. Jun 2009. [MTG] [DOI] Music similarity systems and methods using descriptors. E. Gómez, P. Herrera, P. Cano, J. Janer, J. Serrà, J. Bonada, S. El-Hajj, T. Aussenac, & G. Holmberg. Patent US 2008/300702, published December 31, 2008. Priority nums. US20070946860P-20070628, US20070970109P-20070905, and US20070988714P-20071116. Also published as WO 2009/001202. [FreePatentsOnline] [EspaceNet] Statistical analysis of chroma features in western music predicts human judgments of tonality. J. Serrà, E. Gómez, P. Herrera, & X. Serra. Journal of New Music Research 37(4): 299-309. Dec 2008. [MTG] [DOI] Transposing chroma representations to a common key. J. Serrà, E. Gómez, & P. Herrera. Proc. of the Int. Conf. on The Use of Symbols to Represent Music and Multimedia Objects, pp. 45-48. Oct 2008. [MTG] [UniMi] Improving binary similarity and local alignment for cover song detection. J. Serrà, E. Gómez, & P. Herrera. Music Information Retrieval Evaluation eXchange (MIREX). Sep 2008. [MTG] [MIREX] Chroma binary similarity and local alignment applied to cover song identification. J. Serrà, E. Gómez, P. Herrera, & X. Serra. IEEE Trans. on Audio, Speech and Language Processing 16(6): 1138-1152. Aug 2008. [MTG] [DOI] Audio cover song identification based on tonal sequence alignment. J. Serrà & E. Gómez. Proc. of the IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 61-64. Apr 2008. [MTG] [DOI] A qualitative assessment of measures for the evaluation of a cover song identification system. J. Serrà. Proc. of the Int. Conf. on Music Information Retrieval (ISMIR), pp. 319-322. Sep 2007. [MTG] [ISMIR] A cover song identification system based on sequences of tonal descriptors. J. Serrà & E. Gómez. Music Information Retrieval Evaluation eXchange (MIREX). Sep 2007. [MTG] [MIREX] Music similarity based on sequences of descriptors: tonal features applied to cover song identification. J. Serrà. MSc Thesis. Universitat Pompeu Fabra, Barcelona, Spain. Sep 2007. [MTG] |
