• Corpus ID: 60238484

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

  title={An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics},
  author={Alexander Lerch},
With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, 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. A review of relevant fundamentals in… 
Audio Content Analysis
This chapter focuses on music signals, where ACA is often referred to as Music Information Retrieval (MIR) [84, 14], although the latter additionally encompasses the analysis and generation of symbolic (non-audio) music data such as musical scores.
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
It is shown how music may serve as a vehicle to support education in signal processing, 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.
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.
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.
Blind bandwidth extension using K-means and Support Vector Regression
  • Chih-Wei Wu, M. Vinton
  • Computer Science
    2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
A blind bandwidth extension algorithm for music signals is proposed that applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression.
Augmenting Environmental Interaction in Audio Feedback Systems
Audio feedback is defined as a positive feedback of acoustic signals where an audio input and output form a loop, and may be utilized artistically. This article presents new context-based controls


Multi-pitch and periodicity analysis model for sound separation and auditory scene analysis
  • M. Karjalainen, T. Tolonen
  • Physics
    1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)
  • 1999
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 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, based on the EyesWeb open software platform, able to extract some simple audio cues in real time from single-instrument musical performances. Then, an
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.