• Corpus ID: 60238484

An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics

@inproceedings{Lerch2012AnIT,
  title={An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics},
  author={Alexander Lerch},
  year={2012},
  url={https://api.semanticscholar.org/CorpusID:60238484}
}
This book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike.

Pitch Detection Using Modified Autocorrelation and Web Audio API

The approach to pitch detection using autocorrelation and the Web Audio API represents a promising avenue for future research in music signal processing and has the potential to facilitate the development of new applications in this field.

Audio Content Analysis

This chapter defines musical audio content, introduces the general process of audio content analysis, and surveys basic approaches toaudio content analysis.

Music Performance Analysis: A Survey

The field of Music Performance Analysis (MPA) is surveyed from various perspectives, its significance to the field of MIR is discussed, and opportunities for future research in this field are pointed out.

Music Instrument Identification using Convolutional Neural Networks

The following master thesis presents an Instrument Identification system for polyphonic classical music material including a preprocessing method to reduce crosstalk within multi-track recordings and evaluates two different methods based on sliding classification and multiple instance learning to train a classifier on large time frames but eventually predict on smaller time frames.

Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students

It is demonstrated how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit and how software tools originally developed for music analysis provide students multiple entry points to delve deeper into classical signal processing techniques while bridging the gap between education and cutting-edge research.

Automatic Spatial Audio Scene Classification in Binaural Recordings of Music

It is demonstrated that in addition to the binaural cues, the Mel-frequency cepstral coefficients constitute an important carrier of spatial information, imperative for the classification of spatial audio scenes.

Iracema: a Python library for audio content analysis

An architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them is proposed.

IMPROVEMENT OF AUDIO FEATURE EXTRACTION TECHNIQUES IN TRADITIONAL INDIAN STRING MUSICAL INSTRUMENT

Zero Forcing Equalizer (ZFE) was integrated with three audio feature extraction techniques, namely MFCC-ZFE, LPC-ZFE and ZCR-ZFE in order to improve the performance of the existing techniques.

Robust Audio Content Classification Using Hybrid-Based SMD and Entropy-Based VAD

Experimental results show that the hierarchical ACC system using hybrid feature-based SMD and entropy-based VAD is successfully evaluated against three available datasets and is comparable with existing methods even in a variable noise-level environment.
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Multi-pitch and periodicity analysis model for sound separation and auditory scene analysis

A model for multi-pitch and periodicity analysis of complex audio signals is presented that is more efficient and practical than the Meddis and O'Mard unitary pitch perception model, yet exhibits very similar behavior.

Hierarchical Automatic Audio Signal Classification

The design, implementation, and evaluation of a system for automatic audio signal classification is presented, differentiating between three speech classes, 13 musical genres, and background noise according to audio type.

Camel: a lightweight framework for content-based audio and music analysis

A brief overview of the design decisions and characteristics of CAMEL (Content-based Audio and Music Extraction Library), an easy-to-use C++ framework developed for content-based audio and music analysis.

Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques

This survey discusses the necessity of developing new strategies for classifying sound mixes without a priori separation of sound sources, and evaluates their applicability to the more complex case of describing sound mixes.

Advanced Audio Identification Using MPEG-7 Content Description

A system for automatic identification of audio material from a database of registered works is presented, designed to allow reliable, fast and robust detection ofaudio material with the resources provided by today’s standard computing platforms.

Automatic Song Identification in Noisy Broadcast Audio

A new method to minimize the effects of audio manipulation (i.e. radio edits) and distortions due to broadcast transmissions and is designed to give almost no false positives and achieve very high accuracy.

A HIERARCHICAL APPROACH TO AUTOMATIC MUSICAL GENRE CLASSIFICATION

A system for the automatic classification of audio signals according to audio category is presented, which 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.

Extracting audio cues in real time to understand musical expressiveness

This paper presents a preliminary set of tools, able to extract some simple audio cues in real time from single-instrument musical performances, and an approach to map such cues to an expressive space to distinguish performances characterized by different expressive content.

A Robust Mid-Level Representation for Harmonic Content in Music Signals

It is hoped that by utilizing the notion of a musically-motivated mid-level representation, this work may help bridge the gap between symbolic and audio research.

Evaluation of Features for Audio-to-Audio Alignment

A new method for the objective evaluation of audio-to-audio alignment systems is proposed that enables the use of arbitrary kinds of music as ground truth data and showed that the feature weighting algorithm could improve the alignment accuracies compared to the results of the individual features.
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