An end-to-end machine learning system for harmonic analysis of music

We present a new system for simultaneous estimation of keys, chords, and bass notes from music audio. It makes use of a novel chromagram representation of audio that takes perception of loudness into account. Furthermore, it is fully based on machine learning (instead of expert knowledge), such that it is potentially applicable to a wider range of genres as long as training data is available. As compared to other models, the proposed system is fast and memory efficient, while achieving state-of-the-art performance.Comment: MIREX report and preparation of Journal submissio

AutoRhythmGuitar: Computer-aided Composition for Rhythm Guitar in the Tab Space

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SuMoTED : an intuitive edit distance between rooted unordered uniquely-labelled trees

Defining and computing distances between tree structures is a classical area of study in theoretical computer science, with practical applications in the areas of computational biology, information retrieval, text analysis, and many others. In this paper, we focus on rooted, unordered, uniquely-labelled trees such as taxonomies and other hierarchies. For trees as these, we introduce the intuitive concept of a ‘local move’ operation as an atomic edit of a tree. We then introduce SuMoTED, a new edit distance measure between such trees, defined as the minimal number of local moves required to convert one tree into another. We show how SuMoTED can be computed using a scalable algorithm with quadratic time complexity. Finally, we demonstrate its use on a collection of music genre taxonomies

Leveraging repetition for improved automatic lyric transcription in popular music

Transcribing lyrics from musical audio is a challenging research prob-lem which has not benefited from many advances made in the related field of automatic speech recognition, owing to the prevalent musical accompaniment and differences between the spoken and sung voice. However, one aspect of this problem which has yet to be exploited by researchers is that significant portions of the lyrics will be repeated throughout the song. In this paper we investigate how this information can be leveraged to form a consensus transcription with improved consistency and accuracy. Our results show that improvements can be gained using a variety of techniques, and that relative gains are largest under the most challenging and realistic experimental conditions