1,606 research outputs found
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
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Improving music genre classification using automatically induced harmony rules
We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
Chord recognition systems depend on robust feature extraction pipelines.
While these pipelines are traditionally hand-crafted, recent advances in
end-to-end machine learning have begun to inspire researchers to explore
data-driven methods for such tasks. In this paper, we present a chord
recognition system that uses a fully convolutional deep auditory model for
feature extraction. The extracted features are processed by a Conditional
Random Field that decodes the final chord sequence. Both processing stages are
trained automatically and do not require expert knowledge for optimising
parameters. We show that the learned auditory system extracts musically
interpretable features, and that the proposed chord recognition system achieves
results on par or better than state-of-the-art algorithms.Comment: In Proceedings of the 2016 IEEE 26th International Workshop on
Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Ital
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Representing chord sequences in OWL
Chord symbols and progressions are a common way to describe musical harmony. In this paper we present SEQ, a pattern representation using the Web Ontology Language OWL DL and its application to modelling chord sequences. SEQ provides a logical representation of order information, which is not available directly in OWL DL, together with an intuitive notation. It therefore allows the use of OWL reasoners for tasks such as classification of sequences by patterns and determining subsumption relationships between the patterns. The SEQ representation is used to express distinctive pattern obtained using data mining of multiple viewpoints of chord sequences
Modeling musicological information as trigrams in a system for simultaneous chord and local key extraction
In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Logic-based Modelling of Musical Harmony for Automatic Characterisation and Classification
The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMusic like other online media is undergoing an information explosion. Massive online
music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming
platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to
their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices
offer vast storage capacities and cloud-based apps that can cater any music request. As Paul
Lamere puts it7:
“we can now have a virtually endless supply of music in our pocket. The ‘bottomless iPod’
will have as big an effect on how we listen to music as the original iPod had back in 2001.
But with millions of songs to chose from, we will need help finding music that we want to
hear [...]. We will need new tools that help us manage our listening experience.”
Retrieval, organisation, recommendation, annotation and characterisation of musical data is
precisely what the Music Information Retrieval (MIR) community has been working on for
at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical
fields such as Information Retrieval, Information Systems, Digital Resources and Digital
Libraries but also from the publications presented at the first International Symposium on Music
Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate,
explore and make sense of music collections (Downie et al., 2009). That also includes analytical
tools to suppor
A Survey of AI Music Generation Tools and Models
In this work, we provide a comprehensive survey of AI music generation tools,
including both research projects and commercialized applications. To conduct
our analysis, we classified music generation approaches into three categories:
parameter-based, text-based, and visual-based classes. Our survey highlights
the diverse possibilities and functional features of these tools, which cater
to a wide range of users, from regular listeners to professional musicians. We
observed that each tool has its own set of advantages and limitations. As a
result, we have compiled a comprehensive list of these factors that should be
considered during the tool selection process. Moreover, our survey offers
critical insights into the underlying mechanisms and challenges of AI music
generation
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