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Added to Your Shopping Cart. View on Wiley Online Library. This is a dummy description. Advances in technology, such as MP3 players, the Internet and DVDs, have led to the production, storage and distribution of a wealth of audio signals, including speech, music and more general sound signals and their combinations. MPEG-7 audio tools were created to enable the navigation of this data, by providing an established framework for effective multimedia management.
MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval is a unique insight into the technology, covering the following topics: the fundamentals of MPEG-7 audio, principally low-level descriptors and sound classification and similarity; spoken content description, and timbre, melody and tempo music description tools; existing MPEG-7 applications and those currently being developed; examples of audio technology beyond the scope of MPEG Permissions Request permission to reuse content from this site.
Table of contents List of Acronyms. This yields a sequence of cluster labels one label per texture window. The k-means algorithm takes as argument a user-provided number of clusters speakers. In case that this is not a-priori known, the clustering process is repeated for a range of number of speakers and the Silhouette width criterion [ 11 ] is used to decide about the quality of the clustering result in each case and therefore the optimal number of speakers.
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Smoothing A two-step smoothing process is applied combining a a median filtering on the extracted cluster IDs and b a Viterbi Smoothing step. Table 4 presents an evaluation of the implemented speaker diarization methods on a subset of the widely used Canal9 dataset [ 12 ]. As performance measures, the average cluster purity ACP and the average speaker purity ASP have been adopted, along with their harmonic mean F1 measure.
Performance measures of the implemented speaker diarization method for different initial feature sets. The FLsD method provides a more robust behavior independently from the initial feature space, since it helps to discover a speaker-discriminant subspace. These results prove that the FLsD approach achieves a performance boosting, related to the respective initial feature space.
This proves that the FLsD approach manages to discover a speaker-discriminant subspace.
This is the task of extracting the most representative part of a music recording, which, in popular music, is usually the chorus. The library actually implements a variant of the method proposed in [ 13 ], which is based on finding the maximum of a filtered version of the self-similarity matrix. The results i. The detected diagonal segment defines the two thumbnails, i. Content-based visualization is rather important in many types of multimedia indexing, browsing and recommendation applications, while it can also be used to extract meaningful conclusions regarding the content relationships.
In pyAudioAnalysis, a method that visualizes content similarities between audio signals is provided. The adopted methodology is the following:. Given: a set of audio files e. This information is either used only for visualization or for supervised dimensionality reduction. For example, the filename Blur Charmless Man.
Extract mid-term features and long-term averages in order to produce one feature vector per audio signal. The feature representation can be optionally projected to a lower dimension. A similarity matrix is computed based on the cosine distances of the individual feature vectors. The similarity matrix is used to extract a chordial representation Fig 8 that visualize the content similarities between the audio recordings.
Different colors of the edges and nodes recordings represent different categories artists in our case. The most important of these external dependencies are the following:. Python, as a high-level programming language, introduces a high execution overhead related to C for example , mainly due to its dynamic type functionalities and its interpreted execution.
Table 5 presents the computational demands on three different computers that cover a very wide range of computational power: 1 a standard modern workstation with a Intel Core i CPU 4 cores at 3.
In particular, we present the computational ratios, i. Most of the procedures are executed in high time performance ratios making them practical for real-world problems. The particular values have been calculated for mono—16kHz signals. In addition, a 50 ms short-term window has been adopted. Both of these parameters sampling rate and short-term window step have a linear impact on the computational complexity of all functionalities.
For these methods, the particular ratios have been extracted using a 5-minute signal as input. All basic functionalities can be achieved in a command-line manner through audioAnalysis. Of course, the programmer can also use the individual files for including particular methods via coding.
For example, training a classifier from code can be achieved as follows:. Some examples include:. Content-based multimodal movie recommendation [ 14 ]. Depression estimation [ 15 ]. Speech emotion recognition [ 16 ]. Estimating the quality of urban soundscape using audio analysis [ 17 ]. In this work, pyAudioAnalysis has been used to extract audio features, perform semi-supervised dimensionality reduction and to map these content representations to soundscape quality levels through regression.
In this paper we have presented pyAudioAnalysis, an open-source Python library that implements a wide range of audio analysis functionalities and can be used in several applications. Using pyAudioAnalysis one can classify an unknown audio segment to a set of predefined classes, segment an audio recording and classify homogeneous segments, remove silence areas from a speech recording, estimate the emotion of a speech segment, extract audio thumbnails from a music track, etc. High-level wrappers and command-line usage are also provided so that non-programmers can achieve full functionality.
The range of audio analysis functionalities implemented in the library covers most of the general audio analysis spectrum: classification, regression, segmentation, change detection, clustering and visualization through dimensionality reduction. Therefore pyAudioAnalysis can be used as a basis to most general audio analysis applications. In particular, the main ongoing directions are: a implementation of an audio fingerprinting functionality to be adopted in the context of an audio retrieval system b optimize all feature extraction functionalities by accelerating the critical functions using NVIDIA GPUs parallelization through Cuda programming.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS One. Published online Dec Gianni Pavan, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The author has declared that no competing interests exist. Received Apr 24; Accepted Nov This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
This article has been cited by other articles in PMC. Abstract Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis e.
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Introduction The increasing availability of audio content, through a vast distribution of channels, has resulted in the need for systems that are capable of automatically analyzing this content. Table 1 Related Work. Provides wrappers for Python. Provides the means to perform complex audio signal analysis, transformations and synthesis.
EP1615204A1 - Method for classifying music - Google Patents
Also provides a graphical tool. Can be used as companion matetrial for the book [ 1 ] librosa A Python library that implements some audio features MFCCs, chroma and beat-related features , sound decomposition to harmonic and percussive components, audio effects pitch shifting, etc and some basic communication with machine learning components e. These computations are presented through a couple of audio-related examples.
Open in a separate window. Fig 1. Fig 2. Feature Extraction Features description This Section gives a brief description of the implemented features. Table 2 Audio Features. Index Name Description 1 Zero Crossing Rate The rate of sign-changes of the signal during the duration of a particular frame. It can be interpreted as a measure of abrupt changes. Short and mid-term analysis The aforementioned list of features can be extracted in a short-term basis: the audio signal is first divided into short-term windows frames and for each frame all 34 features are calculated. Tempo-related features Automatic beat induction, i.
Fig 3. Local maxima detection for beat extraction. Fig 4. Beat histogram example. Audio Classification Classification is probably the most important problem in machine learning applications. Audio Regression Regression is the task of estimating the value of an unknown variable instead of distinct class labels , given a respective feature vector.