AN EVALUATION OF AUDIO FEATURE EXTRACTION TOOLBOXES

Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of feature extraction tools presented to the community. An evaluation of existing audio feature extraction libraries was undertaken. Ten libraries and toolboxes were evaluated with the Cranfield Model for evaluation of information retrieval systems, reviewing the cov-erage, effort, presentation and time lag of a system. Comparisons are undertaken of these tools and example use cases are presented as to when toolboxes are most suitable. This paper allows a soft-ware engineer or researcher to quickly and easily select a suitable audio feature extraction toolbox. 1

MPEG-1 bitstreams processing for audio content analysis

In this paper, we present the MPEG-1 Audio bitstreams processing work which our research group is involved in. This work is primarily based on the processing of the encoded bitstream, and the extraction of useful audio features for the purposes of analysis and browsing. In order to prepare for the discussion of these features, the MPEG-1 audio bitstream format is first described. The Application Interface Protocol (API) which we have been developing in C++ is then introduced, before completing the paper with a discussion on audio feature extraction

Piper: Audio Feature Extraction in Browser and Mobile Applications

Piper is a protocol for audio analysis and feature extraction. We propose a data schema and API that can be used to support both remote audio feature extraction services and feature extractors loaded directly into a host application. We provide a means of using existing audio feature extractor implementations with this protocol. In this talk we demonstrate several use-cases for Piper, including an “audio notebook” mobile application using Piper modules to analyse recordings; a web service for remote feature extraction; and the refactoring of an existing desktop application, Sonic Visualiser, to communicate with a Piper service using a simple IPC mechanism

Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval

Structuring music collections by exploiting peers' processing

Music collections are structured in very different ways by different useres. There is not one general taxonomy, but individual, user-specific structures exist. Most users appreciate some support in structering their collection. A large variety of methods has been developed for textual collections. However, audio data are completely different. In this paper, we present a peer to peer scenario where a music collection is enhanced a set of audio data in a node of the user's taxonomy by retrieving (partial) taxonomies of peers. In order to classify audio data into a taxonomy features need to be extracted. Adopting feature extraction to a particular set of classes is effective but not efficient. Hence, we propose again to exploit what has allready been done. Wellsuited feature extraction for one classification task is transferred to similar tasks using a new distance measures. --

Developing a comprehensive framework for multimodal feature extraction

Feature extraction is a critical component of many applied data science workflows. In recent years, rapid advances in artificial intelligence and machine learning have led to an explosion of feature extraction tools and services that allow data scientists to cheaply and effectively annotate their data along a vast array of dimensions---ranging from detecting faces in images to analyzing the sentiment expressed in coherent text. Unfortunately, the proliferation of powerful feature extraction services has been mirrored by a corresponding expansion in the number of distinct interfaces to feature extraction services. In a world where nearly every new service has its own API, documentation, and/or client library, data scientists who need to combine diverse features obtained from multiple sources are often forced to write and maintain ever more elaborate feature extraction pipelines. To address this challenge, we introduce a new open-source framework for comprehensive multimodal feature extraction. Pliers is an open-source Python package that supports standardized annotation of diverse data types (video, images, audio, and text), and is expressly with both ease-of-use and extensibility in mind. Users can apply a wide range of pre-existing feature extraction tools to their data in just a few lines of Python code, and can also easily add their own custom extractors by writing modular classes. A graph-based API enables rapid development of complex feature extraction pipelines that output results in a single, standardized format. We describe the package's architecture, detail its major advantages over previous feature extraction toolboxes, and use a sample application to a large functional MRI dataset to illustrate how pliers can significantly reduce the time and effort required to construct sophisticated feature extraction workflows while increasing code clarity and maintainability

TarsosDSP, a real-time audio processing framework in Java

This paper presents TarsosDSP, a framework for real-time audio analysis and processing. Most libraries and frameworks offer either audio analysis and feature extraction or audio synthesis and processing. TarsosDSP is one of a only a few frameworks that offers both analysis, processing and feature extraction in real-time, a unique feature in the Java ecosystem. The framework contains practical audio processing algorithms, it can be extended easily, and has no external dependencies. Each algorithm is implemented as simple as possible thanks to a straightforward processing pipeline. TarsosDSP's features include a resampling algorithm, onset detectors, a number of pitch estimation algorithms, a time stretch algorithm, a pitch shifting algorithm, and an algorithm to calculate the Constant-Q. The framework also allows simple audio synthesis, some audio effects, and several filters. The Open Source framework is a valuable contribution to the MIR-Community and ideal fit for interactive MIR-applications on Android