Large vocabulary automatic chord estimation using bidirectional long short-term memory recurrent neural network with even chance training

References

• Bello, J. P., & Pickens, J. (2005). A robust mid-level representation for harmonic content in music signals. In Proceedings of the 6th International Society for Music Information Retrieval Conference, ISMIR (Vol. 5, pp. 304–311), London, UK.
   Google Scholar
• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends\textregistered in Machine Learning, 2(1), 1–127.
   Google Scholar
• Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2013). Audio chord recognition with recurrent neural networks. In Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR (pp. 335–340), Curitiba, Brazil.
   Google Scholar
• Brown, J. C. (1991). Calculation of a constant Q spectral transform. The Journal of the Acoustical Society of America, 89(1), 425–434.
   Web of Science ®Google Scholar
• Burgoyne, J. A., Pugin, L., Kereliuk, C., & Fujinaga, I. (2007). A cross-validated study of modelling strategies for automatic chord recognition in audio. In Proceedings of the 8th International Society for Music Information Retrieval Conference, ISMIR (pp. 251–254), Vienna, Austria.
   Google Scholar
• Burgoyne, J. A., Wild, J., & Fujinaga, I. (2011). An expert ground truth set for audio chord recognition and music analysis. In Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR (Vol. 11, pp. 633–638), Miami, FL.
   Google Scholar
• Cannam, C., Mauch, M., Davies, M. E. P., Dixon, S., Landone, C., Noland, K., ... Figueira, L. A. (2013). MIREX 2013 entry: Vamp plugins from the centre for digital music, Curitiba, Brazil.
   Google Scholar
• Catteau, B., Martens, J.-P., & Leman, M. (2007). A probabilistic framework for audio-based tonal key and chord recognition. In B. Decker & H. J, Lenz (Eds.), Advances in data analysis (pp. 637–644). Berlin, Germany: Springer.
   Google Scholar
• Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Editorial: Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1–6.
   Google Scholar
• Cho, T. (2014). Improved techniques for automatic chord recognition from music audio signals (PhD thesis). New York City, NY: New York University.
   Google Scholar
• Cho, T., & Bello, J. P. (2013). MIREX 2013: Large vocabulary chord recognition system using multi-band features and a multi-stream HMM. Music Information Retrieval Evaluation eXchange (MIREX), Curitiba, Brazil.
   Google Scholar
• Deng, J., & Kwok, Y.-K. (2016a). Automatic chord estimation on SeventhsBass chord vocabulary using deep neural network. In Proceedings of the 41th International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.
   Google Scholar
• Deng, J., & Kwok, Y.-K. (2016b). A hybrid Gaussian-HMM-deep-learning approach for automatic chord estimation with very large vocabulary. In Proceedings of the 17th International Society for Music Information Retrieval Conference, ISMIR, New York City, NY.
   Google Scholar
• Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. New York City, NY: Wiley.
   Google Scholar
• Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
   Web of Science ®Google Scholar
• Fujishima, T. (1999). Realtime chord recognition of musical sound: A system using common lisp music. In Proceedings of the 25th International Computer Music Conference (Vol. 1999, pp. 464–467), Beijing, China.
   Google Scholar
• Gómez, E. (2006). Tonal description of music audio signals. PhD Thesis. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona.
   Google Scholar
• Graves, A. (2012). Supervised sequence labelling with recurrent neural networks. Berlin: Springer.
   Google Scholar
• Hamel, P., & Eck, D. (2010). Learning features from music audio with deep belief networks. In Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR (pp. 339–344). Utrecht, The Netherlands.
   Google Scholar
• Harte, C. (2010). Towards automatic extraction of harmony information from music signals (PhD thesis). Department of Electronic Engineering, Queen Mary, University of London, London, UK.
   Google Scholar
• Harte, C., & Sandler, M. (2005). Automatic chord identification using a quantised chromagram. In Audio Engineering Society Convention 118. Barcelona, Spain: Audio Engineering Society.
   Google Scholar
• Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
   PubMed Web of Science ®Google Scholar
• Huang, X., Acero, A., & Hon, H.-W. (2001). Spoken language processing: A guide to theory, algorithm, and system development. (Foreword by: Raj Reddy). Prentice Hall PTR.
   Google Scholar
• Humphrey, E. J., & Bello, J. P. (2012). Rethinking automatic chord recognition with convolutional neural networks. In Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA) (Vol. 2, pp. 357–362). Boca Raton, FL: IEEE.
   Google Scholar
• Humphrey, E. J., Bello, J. P., & LeCun, Y. (2013). Feature learning and deep architectures: New directions for music informatics. Journal of Intelligent Information Systems, 41(3), 461–481.
   Web of Science ®Google Scholar
• Jolliffe, I. (2002). Principal component analysis. New York, NY: Wiley Online Library.
   Google Scholar
• Jordan, M. I. (1986). Attractor dynamics and parallellism in a connectionist sequential machine. In Proceedings of the Eighth Annual Meeting of the Cognitive Science Society (pp. 531–546). Amherst, MA: Lawrence Erlbaum Associates.
   Google Scholar
• Khadkevich, M., & Omologo, M. (2011). Time-frequency reassigned features for automatic chord recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 181–184). Prague, Czech Republic: IEEE.
   Google Scholar
• Korzeniowski, F., & Widmer, G. (2016). Feature learning for chord recognition: The deep chroma extractor. In Proceedings of the 17th International Society for Music Information Retrieval Conference, ISMIR, New York City, NY.
   Google Scholar
• Lang, K. J., Waibel, A. H., & Hinton, G. E. (1990). A time-delay neural network architecture for isolated word recognition. Neural Networks, 3(1), 23–43.
   Web of Science ®Google Scholar
• Lawson, C. L., & Hanson, R. J. (1974). Solving least squares problems (Vol. 15). Prentice-Hall.
   Google Scholar
• LeCun, Y., & Bengio, Y. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), 1995.
   Google Scholar
• Mauch, M. (2010a). Automatic chord transcription from audio using computational models of musical context (PhD thesis). School of Electronic Engineering and Computer Science Queen Mary, University of London.
   Google Scholar
• Mauch, M. (2010b). Simple chord estimate: Submission to the MIREX chord estimation task. Music Information Retrieval Evaluation Exchange (MIREX), Utrecht, Netherlands.
   Google Scholar
• Mauch, M., & Dixon, S. (2010a). Approximate note transcription for the improved identification of difficult chords. In Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR (pp. 135–140), Utrecht, The Netherlands.
   Google Scholar
• Mauch, M., & Dixon, S. (2010b). MIREX 2010: Chord detection using a dynamic Bayesian network. Music Information Retrieval Evaluation Exchange (MIREX), Utrecht, Netherlands.
   Google Scholar
• Mauch, M., & Dixon, S. (2010c). Simultaneous estimation of chords and musical context from audio. IEEE Transactions on Audio, Speech, and Language Processing, 18(6), 1280–1289.
   Web of Science ®Google Scholar
• Ni, Y., McVicar, M., Santos-Rodriguez, R., & De Bie, T. (2012). An end-to-end machine learning system for harmonic analysis of music. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1771–1783.
   Web of Science ®Google Scholar
• Oppenheim, A. V., Willsky, A. S., & Nawab, S. H. (1983). Signals and systems (Vol. 2). Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Papadopoulos, H., & Peeters, G. (2008). Simultaneous estimation of chord progression and downbeats from an audio file. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 121–124). Las Vegas, NV: IEEE.
   Google Scholar
• Papadopoulos, H., & Tzanetakis, G. (2012). Modeling chord and key structure with Markov logic. In Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR (pp. 127–132). Porto, Portugal: Citeseer.
   Google Scholar
• Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(2), 27–49.
   Web of Science ®Google Scholar
• Pauwels, J., & Peeters, G. (2013). Evaluating automatically estimated chord sequences. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 749–753). Vancouver, Canada: IEEE.
   Google Scholar
• Prechelt, L. (1998). Early stopping-but when? In Neural networks: Tricks of the trade (pp. 55–69). Berlin, Germany: Springer.
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive Modeling, 5(3), 1–2.
   Google Scholar
• Rumelhart, D. E., McClelland, J. L., \ & PDP Research Group. (1988). Parallel distributed processing (Vol. 1) . Massachusetts: IEEE.
   Google Scholar
• Ryynänen, M. P., & Klapuri, A. (2005). Polyphonic music transcription using note event modeling. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2005 (pp. 319–322). New York, NY: IEEE.
   Google Scholar
• Sheh, A., & Ellis, D. P. W. (2003). Chord segmentation and recognition using EM-trained hidden Markov models. In Proceedings of the 4th International Society for Music Information Retrieval Conference, ISMIR (pp. 185–191). Baltimore, MD: International Symposium on Music Information Retrieval.
   Google Scholar
• Sigtia, S., Boulanger-Lewandowski, N., & Dixon, S. (2015). Audio chord recognition with a hybrid recurrent neural network. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.
   Google Scholar
• Sigtia, S., & Dixon, S. (2014). Improved music feature learning with deep neural networks. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6959–6963). Florence, Italy: IEEE.
   Google Scholar
• Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
   Google Scholar
• Wakefield, G. H. (1999). Mathematical representation of joint time-chroma distributions. In SPIE’s International Symposium on Optical Science, Engineering, and Instrumentation (pp. 637–645). Denver, CO: International Society for Optics and Photonics.
   Google Scholar
• Weller, A., Ellis, D., & Jebara, T. (2009). Structured prediction models for chord transcription of music audio. In International Conference on Machine Learning and Applications, ICMLA (pp. 590–595). Miami, FL: IEEE.
   Google Scholar
• Werbos, P. J. (1990). Backpropagation through time: What it does and how to do it. Proceedings of the IEEE, 78(10), 1550–1560.
   Web of Science ®Google Scholar
• Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701.
   Google Scholar
• Zhou, X., & Lerch, A. (2015). Chored detection using deep learning. In Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR (Vol. 53).
   Google Scholar