A Physiologically Inspired Method for Audio Classification

Ravindran, Sourabh; Schlemmer, Kristopher; Anderson, David V.

doi:10.1155/asp.2005.1374

Cited by 8 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Eigenspace-based features: in this category, we find Rate-scale-frequency (RSF) features, which describe modulation components present in certain frequency bands of the auditory spectrum, and they are based in the same human auditory model that incorporates the noise-robust audio features (NRAF), as described in the work by Ravindran et al [228] (see Section 5.6.1). RFS represent a compact and decorrelated representation (they are derived performing a Principal Component Analysis stage) of the two-dimensional Wavelet transform applied to the audio spectrum; • Electroencephalogram-based features: or EEG-based features for short, these find application in human-centered favorite music estimation, as introduced by Sawata et al [244].…”

Section: Other Domainsmentioning

confidence: 99%

“…• Noise-robust audio features: or NRAF for short, these features incorporate a specific human auditory model based on a three stage process (a first stage of filtering in the cochlea, transduction of mechanical displacement in electrical activity-log compression in the hair cell stage-, and a reduction stage using decorrelation that mimics the lateral inhibitory network in the cochlear nucleus) (see Ravindran et al [228]). …”

mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

View full text Add to dashboard Cite

Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

show abstract

Section: Other Domainsmentioning

confidence: 99%

mentioning

confidence: 99%

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

2016

View full text Add to dashboard Cite

show abstract

“…For this work, the first eight coefficients are calculated over two successive 250-ms samples (10-msec frame, 31-msec window) and averaged together. Then the first coefficient is discarded to build a seven-coefficient vector describing the sample (Ravindran 2006). A sound source class is an average of vectors created from samples known to contain the sound source.…”

Section: Classification Algorithmmentioning

confidence: 99%

Discovery of sound sources by an autonomous mobile robot

Martinson

Schultz

2009

Auton Robot

View full text Add to dashboard Cite

In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.

show abstract

“…Training data are deconstructed into spectro-temporal acoustic features as they would be in real-time in the device, ranging from simple (e.g., overall level or level within frequency channel) to complex feature sets including those based on perceptual models of human hearing (e.g., modulation frequency and depth; mel-frequency cepstral coefficients, etc. ; Ravindran et al., 2005). For example, complex scenes with speech are often classified based on their spectral profile and temporal envelope (Chen et al., 2014; Feldbusch, 1998; Kates, 1995), their statistical amplitude distribution (Wagener et al., 2008), or their characteristic temporal and/or spectral modulation frequencies (Nordqvist & Leijon, 2004; Ostendorf et al., 1998).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Environment Classification Among Premium Hearing Instruments

et al. 2021

View full text Add to dashboard Cite

Hearing aids classify acoustic environments into multiple, generic classes for the purposes of guiding signal processing. Information about environmental classification is made available to the clinician for fitting, counseling, and troubleshooting purposes. The goal of this study was to better inform scientists and clinicians about the nature of that information by comparing the classification schemes among five premium hearing instruments in a wide range of acoustic scenes including those that vary in signal-to-noise ratio and overall level (dB SPL). Twenty-eight acoustic scenes representing various prototypical environments were presented to five premium devices mounted on an acoustic manikin. Classification measures were recorded from the brand-specific fitting software then recategorized to generic labels to conceal the device company, including (a) Speech in Quiet, (b) Speech in Noise, (c) Noise, and (d) Music. Twelve normal-hearing listeners also classified each scene. The results revealed a variety of similarities and differences among the five devices and the human subjects. Where some devices were highly dependent on input overall level, others were influenced markedly by signal-to-noise ratio. Differences between human and hearing aid classification were evident for several speech and music scenes. Environmental classification is the heart of the signal processing strategy for any given device, providing key input to subsequent decision-making. Comprehensive assessment of environmental classification is essential when considering the cost of signal processing errors, the potential impact for typical wearers, and the information that is available for use by clinicians. The magnitude of differences among devices is remarkable and to be noted.

show abstract

A Physiologically Inspired Method for Audio Classification

Cited by 8 publications

References 10 publications

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

Discovery of sound sources by an autonomous mobile robot

A Comparison of Environment Classification Among Premium Hearing Instruments

Contact Info

Product

Resources

About