Graham E. Poliner

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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 recordings. A frame-level(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)
Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. We discuss melody-roughly, the part a listener might whistle or hum-as one such reduced descriptor of music(More)
Searching and organizing growing digital music collections requires a computational model of music similarity. This paper describes a system for performing flexible music similarity queries using SVM active learning. We evaluated the success of our system by classifying 1210 pop songs according to mood and style (from an online music guide) and by the(More)
As the first decade of the 21st century comes to a close, growth in multimedia delivery infrastructure and public demand for applications built on this backbone are converging like never before. The push towards reaching truly interactive multimedia technologies becomes stronger as our media consumption paradigms continue to change. In this paper, we(More)
The melody of a musical piece—informally, the part you would hum along with—is a useful and compact summary of a full audio recording. The extraction of melodic content has practical applications ranging from content-based audio retrieval to the analysis of musical structure. Whereas previous systems generate transcriptions based on a model of the harmonic(More)
This paper provides an overview of current state-of-the-art approaches for melody extraction from polyphonic audio recordings, and it proposes a methodology for the quantitative evaluation of melody extraction algorithms. We first define a general architecture for melody extraction systems and discuss the difficulties of the problem in hand; then, we review(More)
In this paper, we present methods to improve the generalization capabilities of a classification-based approach to polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances, and the independent classifications are temporally constrained via hidden Markov model post-processing.(More)