8,358 research outputs found
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
Integrating musicological knowledge into a probabilistic framework for chord and key extraction
In this contribution a formerly developed probabilistic framework for the simultaneous detection of chords and keys in polyphonic audio is further extended and validated. The system behaviour is controlled by a small set of carefully defined free parameters. This has permitted us to conduct an experimental study which sheds a new light on the importance of musicological knowledge in the context of chord extraction. Some of the obtained results are at least surprising and, to our knowledge, never reported as such before
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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 Ă 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 Ă 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
The Latin Music Database
In this paper we present the Latin Music Database, a novel database of Latin musical recordings which has been developed for automatic music genre classification, but can also be used in other music information retrieval tasks. The method for assigning genres to the musical recordings is based on human expert perception and therefore capture their tacit knowledge in the genre labeling process. We also present the ethnomusicology of the genres available in the database as it might provide important information for the analysis of the results of any experiment that employs the database
Predicting Audio Advertisement Quality
Online audio advertising is a particular form of advertising used abundantly
in online music streaming services. In these platforms, which tend to host tens
of thousands of unique audio advertisements (ads), providing high quality ads
ensures a better user experience and results in longer user engagement.
Therefore, the automatic assessment of these ads is an important step toward
audio ads ranking and better audio ads creation. In this paper we propose one
way to measure the quality of the audio ads using a proxy metric called Long
Click Rate (LCR), which is defined by the amount of time a user engages with
the follow-up display ad (that is shown while the audio ad is playing) divided
by the impressions. We later focus on predicting the audio ad quality using
only acoustic features such as harmony, rhythm, and timbre of the audio,
extracted from the raw waveform. We discuss how the characteristics of the
sound can be connected to concepts such as the clarity of the audio ad message,
its trustworthiness, etc. Finally, we propose a new deep learning model for
audio ad quality prediction, which outperforms the other discussed models
trained on hand-crafted features. To the best of our knowledge, this is the
first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on
Web Search and Data Mining, 9 page
Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
In this paper, we explore the capabilities of a sound
classification system that combines both a novel FPGA cochlear
model implementation and a bio-inspired technique based on a
trained convolutional spiking network. The neuromorphic
auditory system that is used in this work produces a form of
representation that is analogous to the spike outputs of the
biological cochlea. The auditory system has been developed using
a set of spike-based processing building blocks in the frequency
domain. They form a set of band pass filters in the spike-domain
that splits the audio information in 128 frequency channels, 64
for each of two audio sources. Address Event Representation
(AER) is used to communicate the auditory system with the
convolutional spiking network. A layer of convolutional spiking
network is developed and trained on a computer with the ability
to detect two kinds of sound: artificial pure tones in the presence
of white noise and electronic musical notes. After the training
process, the presented system is able to distinguish the different
sounds in real-time, even in the presence of white noise.Ministerio de EconomĂa y Competitividad TEC2012-37868-C04-0
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