Extracting Emotions from Music Data

Wieczorkowska, Alicja; Synak, Piotr; Lewis, Rory; Raś, Zbigniew W.

doi:10.1007/11425274_47

Cited by 38 publications

(19 citation statements)

References 12 publications

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“…Similar to psychological studies, there is no real agreement on a common model [16]. Some consider a categorical representation based on mutually exclusive basic emotions such as "happiness", "sadness", "anger", "fear" and "tenderness" [19,26,36,39], while others prefer a multi-labeling approach (i.e., using a rich set of adjectives that are not mutually exclusive) like Wieczorkowska [45]. The latter is more difficult to evaluate since they consider many categories.…”

Section: Scientific Backgroundmentioning

confidence: 99%

Indexing music by mood: design and integration of an automatic content-based annotator

Laurier

Meyers

Serrà

et al. 2009

Multimed Tools Appl

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In the context of content analysis for indexing and retrieval, a method for creating automatic music mood annotation is presented. The method is based on results from psychological studies and framed into a supervised learning approach using musical features automatically extracted from the raw audio signal. We present here some of the most relevant audio features to solve this problem. A ground truth, used for training, is created using both social network information systems (wisdom of crowds) and individual experts (wisdom of the few). At the experimental level, we evaluate our approach on a database of 1000 songs. Tests of different classification methods, configurations and optimizations have been conducted, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness against different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed. This real world application demonstrates the usability of this tool to annotate large-scale databases. We also report on a user evaluation in the context of the PHAROS search engine, asking people about the utility, interest and innovation of this technology in real world use cases.

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Section: Scientific Backgroundmentioning

confidence: 99%

Indexing music by mood: design and integration of an automatic content-based annotator

Laurier

Meyers

Serrà

et al. 2009

Multimed Tools Appl

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“…In the context of multi-label classification, various techniques are proposed to identify the emotions in music. In [1], [9], authors survey various machine learning techniques for identifying the emotions in the music. Similarly, multiple binary SVM classifiers are trained to tag the emotions in music in [10].…”

Section: Related Workmentioning

confidence: 99%

Multi-label annotation of music

Ahsan

Kumar

Jawahar

2015

2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR)

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Abstract-Automatic annotation of an audio or a music piece with multiple labels helps in understanding the composition of a music. Such meta-level information can be very useful in applications such as music transcription, retrieval, organization and personalization. In this work, we formulate the problem of annotation as multi-label classification which is considerably different from that of a popular single (binary or multi-class) label classification. We employ both the nearest neighbour and max-margin (SVM) formulations for the automatic annotation. We consider K-NN and SVM that are adapted for multi-label classification using one-vs-rest strategy and a direct multi-label classification formulation using ML-KNN and M3L. In the case of music, often the signatures of the labels (e.g. instruments and vocal signatures) are fused in the features. We therefore propose a simple feature augmentation technique based on non-negative matrix factorization (NMF) with an intuition to decompose a music piece into its constituent components. We conducted our experiments on two data sets -Indian classical instruments dataset and Emotions dataset [1], and validate the methods.

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“…Similarly to psychological studies, there is no real agreement on a common model. Some consider a categorical representation based on basic emotions [10,23,20,13], while others prefer a multi-labeling approach like Wieczorkowska [26]. The latter is more difficult to evaluate since they consider many categories.…”

Section: Scientific Backgroundmentioning

confidence: 99%

Music Mood Annotator Design and Integration

Laurier

Meyers

Serrà

et al. 2009

2009 Seventh International Workshop on Content-Based Multimedia Indexing

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A robust and efficient technique for automatic music mood annotation is presented. A song's mood is expressed by a supervised machine learning approach based on musical features extracted from the raw audio signal. A ground truth, used for training, is created using both social network information systems and individual experts. Tests of 7 different classification configurations have been performed, showing that Support Vector Machines perform best for the task at hand. Moreover, we evaluate the algorithm robustness to different audio compression schemes. This fact, often neglected, is fundamental to build a system that is usable in real conditions. In addition, the integration of a fast and scalable version of this technique with the European Project PHAROS is discussed.

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Extracting Emotions from Music Data

Cited by 38 publications

References 12 publications

Indexing music by mood: design and integration of an automatic content-based annotator

Indexing music by mood: design and integration of an automatic content-based annotator

Multi-label annotation of music

Music Mood Annotator Design and Integration

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