2,405 research outputs found
Enhancing timbre model using MFCC and its time derivatives for music similarity estimation
One of the popular methods for content-based music similarity estimation is to model timbre with MFCC as a single multivariate Gaussian with full covariance matrix, then use symmetric Kullback-Leibler divergence. From the field of speech recognition, we propose to use the same approach on the MFCCs’ time derivatives to enhance the timbre model. The Gaussian models for the delta and acceleration coefficients are used to create their respective distance matrix. The distance matrices are then combined linearly to form a full distance matrix for music similarity estimation. In our experiments on two datasets, our novel approach performs better than using MFCC alone.Moreover, performing genre classification using k-NN showed that the accuracies obtained are already close to the state-of-the-art
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh
International Conferences on Pervasive Patterns and Applications (PATTERNS
2015), Mar 2015, Nice, Franc
Listening to features
This work explores nonparametric methods which aim at synthesizing audio from
low-dimensionnal acoustic features typically used in MIR frameworks. Several
issues prevent this task to be straightforwardly achieved. Such features are
designed for analysis and not for synthesis, thus favoring high-level
description over easily inverted acoustic representation. Whereas some previous
studies already considered the problem of synthesizing audio from features such
as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit
formula used to compute those features in order to inverse them. Here, we
instead adopt a simple blind approach, where arbitrary sets of features can be
used during synthesis and where reconstruction is exemplar-based. After testing
the approach on a speech synthesis from well known features problem, we apply
it to the more complex task of inverting songs from the Million Song Dataset.
What makes this task harder is twofold. First, that features are irregularly
spaced in the temporal domain according to an onset-based segmentation. Second
the exact method used to compute these features is unknown, although the
features for new audio can be computed using their API as a black-box. In this
paper, we detail these difficulties and present a framework to nonetheless
attempting such synthesis by concatenating audio samples from a training
dataset, whose features have been computed beforehand. Samples are selected at
the segment level, in the feature space with a simple nearest neighbor search.
Additionnal constraints can then be defined to enhance the synthesis
pertinence. Preliminary experiments are presented using RWC and GTZAN audio
datasets to synthesize tracks from the Million Song Dataset.Comment: Technical Repor
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