Auditory masking and MPEG-1 audio compression

Ambikairajah, Eliathamby; Davis, Andrew G.; Wong, W. T. K.

doi:10.1049/ecej:19970403

Cited by 39 publications

(28 citation statements)

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“…(a) By definition, the narrowband speech signal is band limited to 300-3400 Hz and 200-3200 Hz in Europe and USA, respectively [11]. However, this bandwidth is considered to be 200-3400 Hz, in general [1].…”

Section: Perceptual Quantizermentioning

confidence: 99%

“…To avoid this, we act as follows. We calculate the average energy of the coefficients of the narrow-band region (sub-bands [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. If this is smaller than a given value e 1 , we add one bit to the number of the allocated bits of the narrow sub-bands whose average energies are smaller than a defined value e 2 .…”

Section: Perceptual Quantizermentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Ghahabi

Savoji

2011

ISRN Signal Processing

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A fast, efficient, and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising (1) embedded zero-tree wavelet (EZW) and (2) the set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.

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Section: Perceptual Quantizermentioning

confidence: 99%

Section: Perceptual Quantizermentioning

confidence: 99%

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Ghahabi

Savoji

2011

ISRN Signal Processing

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“…Using the masking threshold curve [1,4], we can determine the acceptably maximal quantification error in each sub-band to escape from hearing the distorted parts, caused by quantification.…”

Section: The Frequency and Temporal Masking Propertiesmentioning

confidence: 99%

Audio-Signal Authenticating System Based on Asymmetric Signature Schemes

Wang

Liao

Chen

2007

2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07)

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In this paper, we propose a simple and highly secure signature scheme to protect the copyright of digital-signal. Employing the masking property of human ears that most of people can't hear the signal of frequency of 1-20Hz, we use the encoding scheme to change the energy of frequency to conceal a signature. The embedded signature values will not influence the quality of original audio-signal. We can raise the integrity of audio-signal because the scheme can definitely figure out if a signal is tampered or not. In designed method, we extract a characteristic value M from the audio-signal, then apply the RSA signature scheme to transform the characteristic into signature S and conceal it in audio-signal. In the requirement of verifying copyright, our scheme retrieves the signature S and uses RSA signature scheme to decrypt it and obtain a characteristic value M . By comparing the value M with the original M , if the signal was tampered, the characteristic M and the original M should be distinct; otherwise, they are the same. In order to authenticate the audio-signal, the proposed method can independently retrieve the characteristic value M quickly without applying the original audio-signal, even the signal was tampered. Therefore, the security of the scheme is improved.

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“…Weak signal components become inaudible by the presence of stronger signal components in the same critical band that precede or follow them in time, and this is called temporal masking. When the signal precedes the masker in time, it is called pre-masking; when the signal follows the masker in time, the condition is called post-masking [1,9,10]. A strong signal can mask a weaker signal that occurs after it and a weaker signal that occurs before it.…”

Section: C968mentioning

confidence: 99%

Auditory modelling for speech processing in the perceptual domain

Lin

Ambikairajah²,

Holmes³

2004

ANZIAMJ

Self Cite

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The human hearing system is the most robust speech processor despite noisy environments. This work presents a new computational model for our auditory system by exploring the psychoacoustical masking properties. The model is then applied to speech coding in the perceptual domain. The coding algorithm is capable of producing high quality coded speech and audio, which account for temporal as well as spectral details. The proposed filterbank is also applied to speech denoising in the perceptual domain. The enhanced speech is of good perceptual quality.

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Auditory masking and MPEG-1 audio compression

Cited by 39 publications

References 0 publications

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Adaptive Variable Degree- Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet Packet Transformed Audio and High-Quality Speech

Audio-Signal Authenticating System Based on Asymmetric Signature Schemes

Auditory modelling for speech processing in the perceptual domain

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