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We introduce the Million Song Dataset, a freely-available collection of audio features and metadata for a million contemporary popular music tracks. We describe its creation process, its content, and its possible uses. Attractive features of the Million Song Database include the range of existing resources to which it is linked, and the fact that it is the(More)
Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decor-related acoustic feature vectors that correspond to individual sub-word units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units(More)
We have collected a corpus of data from natural meetings that occurred at the International Computer Science Institute (ICSI) in Berkeley, California over the last three years. The corpus contains audio recorded simultaneously from head-worn and table-top microphones , word-level transcripts of meetings, and various meta-data on participants, meetings, and(More)
Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existing corpora for video analysis lack scale and/or content diversity, and thus limited the needed progress in this critical area. In this paper, we describe and release a new database called CCV, containing 9,317 web videos over 20 semantic(More)
Automatic extraction of content description from commercial audio recordings has a number of important applications, from indexing and retrieval through to novel musicological analyses based on very large corpora of recorded performances. Chord sequences are a description that captures much of the character of a piece in a compact form and using a modest(More)
Recommended by Masataka Goto We present a discriminative model for polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances. The classifier outputs are temporally constrained via hidden Markov models, and the proposed system is used to transcribe both synthesized and real piano(More)
This paper describes a system, referred to as model-based expectation-maximization source separation and localization (MESSL), for separating and localizing multiple sound sources from an underdetermined reverberant two-channel recording. By clustering individual spectrogram points based on their interaural phase and level differences, MESSL generates masks(More)
Beat tracking – i.e. deriving from a music audio signal a sequence of beat instants that might correspond to when a human listener would tap his foot – involves satisfying two constraints: On the one hand, the selected instants should generally correspond to moments in the audio where a beat is indicated, for instance by the onset of a note played by one of(More)
Large music collections, ranging from thousands to millions of tracks, are unsuited to manual searching, motivating the development of automatic search methods. When different musicians perform the same underlying song or piece, these are known as `cover' versions. We describe a system that attempts to identify such a relationship between music audio(More)
The statistical theory of speech recognition introduced several decades ago has brought about low word error rates for clean speech. However, it has been less successful in noisy conditions. Since extraneous acoustic sources are present in virtually all everyday speech communication conditions, the failure of the speech recognition model to take noise into(More)