19,936 research outputs found
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
JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
We propose a novel approach for the generation of polyphonic music based on
LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord
progression based on a chord embedding. A second LSTM then generates polyphonic
music from the predicted chord progression. The generated music sounds pleasing
and harmonic, with only few dissonant notes. It has clear long-term structure
that is similar to what a musician would play during a jam session. We show
that our approach is sensible from a music theory perspective by evaluating the
learned chord embeddings. Surprisingly, our simple model managed to extract the
circle of fifths, an important tool in music theory, from the dataset.Comment: Paper presented at the 29th International Conference on Tools with
Artificial Intelligence, ICTAI 2017, Boston, MA, US
Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription
In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated
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