A system for the automatic classification of audio signals according to audio category is presented. The signals are recognized as speech, background noise and one of 13 musical genres. A large number of audio features are evaluated for their suitability in such a classification task, including well-known physical and perceptual features, audio descriptors defined in the MPEG-7 standard, as well as new features proposed in this work. These are selected with regard to their ability to distinguish between a given set of audio types and to their robustness to noise and bandwidth changes. In contrast to previous systems, the feature selection and the classification process itself are carried out in a hierarchical way. This is motivated by the numerous advantages of such a tree-like structure, which include easy expansion capabilities, flexibility in the design of genre-dependent features and the ability to reduce the probability of costly errors. The resulting application is evaluated with respect to classification accuracy and computational costs.

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This paper presents FEAPI, an easy-to-use platform-independent plugin application programming interface (API) for the extraction of low level features from audio in PCM format in the context of music information retrieval software. The need for and advantages of using an open and well-defined plugin interface are outlined in this paper and an overview of the API itself and its usage is given.

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The tonalness spectrum shows the likelihood of a spectral bin being part of a tonal or non-tonal component. It is a non-binary measure based on a set of established spectral features. An easily extensible framework for the computation, selection, and combination of features is introduced. The results are evaluated and compared in two ways. First with a data set of synthetically generated signals but also with real music signals in the context of a typical MIR application.

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In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least informative features are identified through feature selection methods and are then tested using Support Vector Machines on five genre datasets concerning classification accuracy against a baseline feature set. Results show that the presented features provide comparable classification accuracy with respect to other genre classification approaches using periodicity histograms and display a performance close to that of much more elaborate up-to-date approaches for rhythm description. The use of bar boundary annotations for the texture frames has provided an improvement for the dance-oriented Ballroom dataset. The comparably small number of descriptors and the possibility of evaluating the influence of specific signal components to the general rhythmic content encourage the further use of the method in rhythm description tasks.

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The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many music information retrieval tasks. We introduce three approaches to incorporate the features, including two latent space regularization methods and one naive concatenation method. Our preliminary results show that our proposed approaches could improve some evaluation metrics for music source separation. In this work, we also include a discussion of our proposed approaches, such as the pros and cons of each approach, and the potential extension/improvement.

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