A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
Dimensionality Reduction by Learning an Invariant Mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Signal Processing Methods for Music Transcription
Signal Processing Methods for Music Transcription
Music information retrieval using social tags and audio
IEEE Transactions on Multimedia - Special section on communities and media computing
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Simultaneous estimation of chords and musical context from audio
IEEE Transactions on Audio, Speech, and Language Processing
Sound retrieval and ranking using sparse auditory representations
Neural Computation
Web-Scale Multimedia Analysis: Does Content Matter?
IEEE MultiMedia
Recognizing chords with EDS: part one
CMMR'05 Proceedings of the Third international conference on Computer Music Modeling and Retrieval
Extracting Predominant Local Pulse Information From Music Recordings
IEEE Transactions on Audio, Speech, and Language Processing
Non-Linear Semantic Embedding for Organizing Large Instrument Sample Libraries
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
Learning invariant feature hierarchies
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Rethinking Automatic Chord Recognition with Convolutional Neural Networks
ICMLA '12 Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 02
Classification accuracy is not enough
Journal of Intelligent Information Systems
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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As we look to advance the state of the art in content-based music informatics, there is a general sense that progress is decelerating throughout the field. On closer inspection, performance trajectories across several applications reveal that this is indeed the case, raising some difficult questions for the discipline: why are we slowing down, and what can we do about it? Here, we strive to address both of these concerns. First, we critically review the standard approach to music signal analysis and identify three specific deficiencies to current methods: hand-crafted feature design is sub-optimal and unsustainable, the power of shallow architectures is fundamentally limited, and short-time analysis cannot encode musically meaningful structure. Acknowledging breakthroughs in other perceptual AI domains, we offer that deep learning holds the potential to overcome each of these obstacles. Through conceptual arguments for feature learning and deeper processing architectures, we demonstrate how deep processing models are more powerful extensions of current methods, and why now is the time for this paradigm shift. Finally, we conclude with a discussion of current challenges and the potential impact to further motivate an exploration of this promising research area.