Asymmetry of masking between noise and tone

Hellman, Rhona P.

doi:10.3758/bf03206257

Cited by 109 publications

(54 citation statements)

References 16 publications

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“…When the signal and masker are centered at the same frequency, the amount of masking is about 20 dB lower than for the TN and TT conditions. This asymmetry of masking has been reported previously and explained by temporal envelope fluctuations introduced by the noise signal ͑e.g., Hellman, 1972;Hall, 1997;Moore et al, 1998;Gockel et al, 2002;. The simulated patterns agree very well with the data, except for signal ͑center͒ frequencies of 500 and 750 Hz at the high masker level, where masking is overestimated by about 10 dB.…”

Section: Spectral Masking Patterns With Narrowband Signals and Massupporting

confidence: 73%

A computational model of human auditory signal processing and perception

Jepsen

Ewert

Dau

2008

The Journal of the Acoustical Society of America

163

148

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Document VersionPublisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA):Jepsen, M. L., Ewert, S. D., & Dau, T. (2008 A model of computational auditory signal-processing and perception that accounts for various aspects of simultaneous and nonsimultaneous masking in human listeners is presented. The model is based on the modulation filterbank model described by Dau et al. ͓J. Acoust. Soc. Am. 102, 2892 ͑1997͔͒ but includes major changes at the peripheral and more central stages of processing. The model contains outer-and middle-ear transformations, a nonlinear basilar-membrane processing stage, a hair-cell transduction stage, a squaring expansion, an adaptation stage, a 150-Hz lowpass modulation filter, a bandpass modulation filterbank, a constant-variance internal noise, and an optimal detector stage. The model was evaluated in experimental conditions that reflect, to a different degree, effects of compression as well as spectral and temporal resolution in auditory processing. The experiments include intensity discrimination with pure tones and broadband noise, tone-in-noise detection, spectral masking with narrow-band signals and maskers, forward masking with tone signals and tone or noise maskers, and amplitude-modulation detection with narrow-and wideband noise carriers. The model can account for most of the key properties of the data and is more powerful than the original model. The model might be useful as a front end in technical applications.

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Section: Spectral Masking Patterns With Narrowband Signals and Massupporting

confidence: 73%

A computational model of human auditory signal processing and perception

Jepsen

Ewert

Dau

2008

The Journal of the Acoustical Society of America

163

148

View full text Add to dashboard Cite

show abstract

“…Depending on the shape of the magnitude spectrum, the presence of certain spectral energy will mask the presence of other spectral energy. Although arbitrary audio spectra may contain complex simultaneous masking scenarios, for the purposes of shaping coding distortions it is convenient to distinguish between only two types of simultaneous masking, namely tone-masking-noise [40], and noise-masking-tone [41]. In the first case, a tone occurring at the center of a critical band masks noise of any subcritical bandwidth or shape, provided the noise spectrum is below a predictable threshold directly related to the strength of the masking tone.…”

Section: Simultaneous Masking and The Spread Of Maskingmentioning

confidence: 99%

Perceptual coding of digital audio

2000

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I. INTRODUCTIONAudio coding or audio compression algorithms are used to obtain compact digital representations of high-fidelity (wideband) audio signals for the purpose of efficient transmission or storage. The central objective in audio coding is to represent the signal with a minimum number of bits while achieving transparent signal reproduction, i.e., generating output audio that cannot be distinguished from the original input, even by a sensitive listener ("golden ears"). This paper gives a review of algorithms for transparent coding of high-fidelity audio.The introduction of the compact disk (CD) in the early eighties [1] brought to the fore all of the advantages of digital audio representation, including unprecedented high-fidelity, dynamic range, and robustness. These advantages, however, came at the expense of high data rates. Conventional CD and digital audio tape (DAT) systems are typically sampled at either 44.1 or 48 kilohertz (kHz) using pulse code modulation (PCM) with a sixteen bit sample resolution. This results in uncompressed data rates of 705.6/768 kilobits per second (kbps) for a monaural channel, or 1.41/1.54 megabits per second (Mbps) for a stereo pair at 44.1/48 kHz, respectively. Although high, these data rates were accommodated successfully in first generation digital audio applications such as CD and DAT. Unfortunately, second generation multimedia applications and wireless systems in particular are often subject to bandwidth and cost constraints that are incompatible with high data rates. Because of the success enjoyed by the first generation, however, end users have come to expect "CD-quality" audio reproduction from any digital system. Therefore, new network and wireless multimedia digital audio systems must reduce data rates without compromising reproduction quality. These and other considerations have motivated considerable research during the last decade towards formulation of compression schemes that can satisfy simultaneously the conflicting demands of high compression ratios and transparent reproduction quality for high-fidelity audio signals

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“…Audio fingerprinting must be resilient to all the above distortions without loosing specificity. Several features have been used for audio-fingerprinting purposes, among them, the Mel-frequency Cepstral coefficients (MFCC) [3], [4]; the Spectral Flatness Measure (SFM) [5]; tonality [6] and chroma values [7], most of them are analyzed in depth in [8]. Recently in [9,10] the use of entropy as the sole feature for audio fingerprinting proved to be much more robust to severe degradations outperforming previous approaches.…”

Section: Related Workmentioning

confidence: 99%

Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

Camarena-Ibarrola¹,

Chávez²,

Téllez³

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Monitoring media broadcast content has deserved a lot of attention lately from both academy and industry due to the technical challenge involved and its economic importance (e.g. in advertising). The problem pose a unique challenge from the pattern recognition point of view because a very high recognition rate is needed under non ideal conditions. The problem consist in comparing a small audio sequence (the commercial ad) with a large audio stream (the broadcast) searching for matches.In this paper we present a solution with the Multi-Band Spectral Entropy Signature (MBSES) which is very robust to degradations commonly found on amplitude modulated (AM) radio. Using the MBSES we obtained perfect recall (all audio ads occurrences were accurately found with no false positives) in 95 hours of audio from five different am radio broadcasts. Our system is able to scan one hour of audio in 40 seconds if the audio is already fingerprinted (e.g. with a separated slave computer), and it totaled five minutes per hour including the fingerprint extraction using a single core off the shelf desktop computer with no parallelization.

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Asymmetry of masking between noise and tone

Cited by 109 publications

References 16 publications

A computational model of human auditory signal processing and perception

A computational model of human auditory signal processing and perception

Perceptual coding of digital audio

Robust Radio Broadcast Monitoring Using a Multi-Band Spectral Entropy Signature

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