Time Series Models for Semantic Music Annotation

Coviello, Emanuele; Chan, Antoni B.; Lanckriet, Gert R. G.

doi:10.1109/tasl.2010.2090148

Cited by 44 publications

(31 citation statements)

References 21 publications

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“…In the first stage, such algorithms infer higherlevel information from content features, such as term weight vector representations. These representations are then fed into a machine learning algorithm to learn semantic labels [9,17]. For instance, Miotto et al [17] first model semantic multinomials over tags based on music content features.…”

Section: Related Workmentioning

confidence: 99%

Location-aware music recommendation using auto-tagging and hybrid matching

Kaminskas

Ricci

Schedl

2013

Proceedings of the 7th ACM Conference on Recommender Systems

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We propose a novel approach to context-aware music recommendation -recommending music suited for places of interest (POIs). The suggested hybrid approach combines two techniques -one based on representing both POIs and music with tags, and the other based on the knowledge of the semantic relations between the two types of items.We show that our approach can be scaled up using a novel music auto-tagging technique and we compare it in a live user study to: two non-hybrid solutions, either based on tags or on semantic relations; and to a context-free but personalized recommendation approach. In the considered scenario, i.e., a situation defined by a context (the POI), we show that personalization (via music preference) is not sufficient and it is important to implement effective adaptation techniques to the user's context. In fact, we show that the users are more satisfied with the recommendations generated by combining the tag-based and knowledge-based context adaptation techniques, which exploit orthogonal types of relations between places and music tracks.

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Section: Related Workmentioning

confidence: 99%

Location-aware music recommendation using auto-tagging and hybrid matching

Kaminskas

Ricci

Schedl

2013

Proceedings of the 7th ACM Conference on Recommender Systems

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“…Although quite a few MIR researchers suggest such a combination [2,1,3,12,5], a systematic evaluation of combining state-of-the-art audio and web similarity estimators is still missing, hence provided here.…”

Section: Hybrid Music Retrievalmentioning

confidence: 99%

Hybrid retrieval approaches to geospatial music recommendation

Schedl

Schnitzer

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recent advances in music retrieval and recommendation algorithms highlight the necessity to follow multimodal approaches in order to transcend limits imposed by methods that solely use audio, web, or collaborative filtering data. In this paper, we propose hybrid music recommendation algorithms that combine information on the music content, the music context, and the user context, in particular, integrating location-aware weighting of similarities. Using state-ofthe-art techniques to extract audio features and contextual web features, and a novel standardized data set of music listening activities inferred from microblogs (MusicMicro), we propose several multimodal retrieval functions.The main contributions of this paper are (i) a systematic evaluation of mixture coefficients between state-of-the-art audio features and web features, using the first standardized microblog data set of music listening events for retrieval purposes and (ii) novel geospatial music recommendation approaches using location information of microblog users, and a comprehensive evaluation thereof.

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“…The protocol defines training and evaluation sets, which consist of 729 audio files each. The experiments on music tagging were conducted following the experimental procedure defined in [26]. That is, 78 tags, which have been employed to annotate at least 50 music recordings in the CAL500 dataset, were used in the experiments by applying fivefold cross-validation.…”

Section: Datasets and Evaluation Proceduresmentioning

confidence: 99%

“…That is, F 1 = 2 · precision·recall precision+recall yields a scalar measure of overall annotation performance. If a tag is never selected for annotation, then following [1,26], the corresponding precision (that otherwise would be undefined) is set to the tag prior to the training set, which equals the performance of a random classifier. In the music tagging experiments, the length of the class indicator vector returned by the LRSMs as well as the MLSRC, the Rank-SVMs, the MLkNN, and the PARAFAC2-based autotagging method was set to 10 as in [1,26].…”

Section: Datasets and Evaluation Proceduresmentioning

confidence: 99%

“…For instance, music tag prediction may be treated as a set of binary classification problems, where standard classifiers, such as the SVMs [12,14] or ada-boost [25], can be applied. Furthermore, probabilistic autotagging systems have been proposed, attempting to infer the correlations or joint probabilities between the tags and the audio features [1,9,26].…”

Section: Introductionmentioning

confidence: 99%

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Music classification by low-rank semantic mappings

Panagakis

Kotropoulos

2013

J AUDIO SPEECH MUSIC PROC.

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A challenging open question in music classification is which music representation (i.e., audio features) and which machine learning algorithm is appropriate for a specific music classification task. To address this challenge, given a number of audio feature vectors for each training music recording that capture the different aspects of music (i.e., timbre, harmony, etc.), the goal is to find a set of linear mappings from several feature spaces to the semantic space spanned by the class indicator vectors. These mappings should reveal the common latent variables, which characterize a given set of classes and simultaneously define a multi-class linear classifier that classifies the extracted latent common features. Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms. Since the nuclear norm imposes rank constraints to the learnt mappings, the proposed method is referred to as low-rank semantic mappings (LRSMs). The performance of the LRSMs in music genre, mood, and multi-label classification is assessed by conducting extensive experiments on seven manually annotated benchmark datasets. The reported experimental results demonstrate the superiority of the LRSMs over the classifiers that are compared to. Furthermore, the best reported classification results are comparable with or slightly superior to those obtained by the state-of-the-art task-specific music classification methods.

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Time Series Models for Semantic Music Annotation

Cited by 44 publications

References 21 publications

Location-aware music recommendation using auto-tagging and hybrid matching

Location-aware music recommendation using auto-tagging and hybrid matching

Hybrid retrieval approaches to geospatial music recommendation

Music classification by low-rank semantic mappings

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