When generating musical sound on a digital computer, it is important to have a good model whose parameters provide a rich source of meaningf ul sound transformations. Three basic model types are used widely today far musical sound generation: instrument models, spectrum models, and abstract models. Instrument models attempt to parameterize a sound at its source, such as a violin, clarinet, or vocal tract. Spectrum models attempt to parameterize a sound at the basilar membrane of the ear, discarding whatever information the ear seems to discard in the spectrum. Abstract models, such as FM, attempt to provide musically useful parameters in an abstract formula. This paper addresses the second category of synthesis technique: spectrum modeling. It describes a technique called spectral modeling synthesis (SMS), that models time-varying spectra as ( 1) a collection of sinusoids controlled through time by piecewise linear amplitude and frequency envelopes ( the de- terministic part), and (2) a time-varying filtered noise component ( the stochastic part). The analysis procedure first extracts the sinusoidal trajectories by tracking peaks in a sequence of short-time Fourier transforms. These peaks are then removed by spectral subtraction. The remaining "noise floor" is then modeled as white noise through a time- varying filter. A piecewise linear approximation to the upper spectral envelope of the noise is computed far each successive spectrum, and the stochastic part is synthesized by means of the overlap- add technique. The SMStechnique has proved to give general, high quality transformations far a wide variety ofmusical signals

Freesound is an online collaborative sound database where people with diverse interests share recorded sound samples under Creative Commons licenses. It was started in 2005 and it is being maintained to support diverse research projects and as a service to the overall research and artistic community. In this demo we want to introduce Freesound to the multimedia community and show its potential as a research resource. We begin by describing some general aspects of Freesound, its architecture and functionalities, and then explain potential usages that this framework has for research applications.

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches.

Music genre labels are useful to organize songs, albums, and artists into broader groups that share similar musical characteristics. In this work, an approach to learn and combine multimodal data representations for music genre classification is proposed. Intermediate representations of deep neural networks are learned from audio tracks, text reviews, and cover art images, and further combined for classification. Experiments on single and multi-label genre classification are then carried out, evaluating the effect of the different learned representations and their combinations. Results on both experiments show how the aggregation of learned representations from different modalities improves the accuracy of the classification, suggesting that different modalities embed complementary information. In addition, the learning of a multimodal feature space increases the performance of pure audio representations, which may be specially relevant when the other modalities are available for training, but not at prediction time. Moreover, a proposed approach for dimensionality reduction of target labels yields major improvements in multi-label classification not only in terms of accuracy, but also in terms of the diversity of the predicted genres, which implies a more fine-grained categorization. Finally, a qualitative analysis of the results sheds some light on the behavior of the different modalities on the classification task.