Application of adaptive wavelets for speech coding

Kadambe, S.; Srinivasan, Pramila

doi:10.1109/tfsa.1994.467272

Cited by 13 publications

(15 citation statements)

References 7 publications

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“…The wavelet packet decomposition applies the transform step to both the low pass and the high pass result. It allo ws simultaneous use of long-time interval fo r low-frequency informat ion and shorttime interval for high-frequency informat ion [6]. In wavelet packet decomposition, each detail coefficient vector is also decomposed into two parts using the same approach as in approximation vector splitting.…”

Section: The Wavelet Packet Decompositionmentioning

confidence: 99%

Financial Transactions in ATM Machines using Speech Signals

Sunny¹

2017

IJERA

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Speech is the natural and simp lest way of co mmunicat ion and Speech Recognition is a fascinating application of Dig ital Signal Processing which has many real-world applicat ions. In this paper, a speech recognition system is developed for Automated Teller Mach ines (ATMs) using Wavelet Packet Decomposition (WPD) and Artificial Neural Networks (ANN). Speech signals are one-dimensional and are random in nature. ATM machines communicate with the customers using the stored speech samples and the user communicates with the mach ine using spoken digits. Daubechies wavelets are employed here. A mu ltilayer neural network trained with back propagation training algorith m is used for classification purpose. The proposed method is implemented for 500 speakers uttering 10 spoken digits in English. The experimental results show good recognition accuracy of 87.38% and the efficiency of co mbin ing these two techniques.

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Section: The Wavelet Packet Decompositionmentioning

confidence: 99%

Financial Transactions in ATM Machines using Speech Signals

Sunny¹

2017

IJERA

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“…Ma et al [11] use wavelets to scramble analog signals in 2D space and it is highly secure in temporal and spatial domain. Kadambe [8] uses adaptive wavelets for speech scrambling. However, there has been no work on compressed domain for audio scrambling.…”

Section: Related Workmentioning

confidence: 99%

Progressive Audio Scrambling in Compressed Domain

Wan

Kankanhalli

2008

IEEE Trans. Multimedia

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Abstract-Audio scrambling can be employed to ensure confidentiality in audio distribution. We first describe scrambling for raw audio using the Discrete Wavelet Transform (DWT) first and then focus on MP3 audio scrambling. We perform scrambling based on a set of keys which allows for a set of audio outputs having different qualities. During descrambling, the number of keys provided and the number of rounds of descrambling performed will decide the audio output quality. We also perform scrambling by using multiple keys on the MP3 audio format. With a subset of keys, we can descramble to obtain a low quality audio. However, we can obtain the original quality audio by using all of the keys. Our experiments show that the proposed algorithms are effective, fast, simple to implement while providing flexible control over the progressive quality of the audio output. The security level provided by the scheme is sufficient for protecting MP3 music content.

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“…In [2] then, two classification examples are considered with application to speech; classification of unvoiced phonemes and speaker identification.…”

Section: Phoneme and Speaker Classification Using Adaptive Waveletsmentioning

confidence: 99%

“…The indications are that the Wavelet Transform and its variants are useful in speech recognition due to their good feature localisation but furthermore because more accurate (non-linear) speech production models can be assumed [2]. The adaptive nature of some existing techniques results in a reduction of error due to inter/intra speaker variation.…”

Section: Introductionmentioning

confidence: 99%

Wavelet based feature extraction for phoneme recognition

Long

Datta²

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

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In an effort to provide a more efficient representation of the acoustical speech signal in the pre-classification stage of a speech recognition system, we consider the application of the Best-Basis Algorithm of Coifman and Wickerhauser. This combines the advantages of using a smooth, compactly-supported wavelet basis with an adaptive time-scale analysis dependent on the problem at hand. We start by briefly reviewing areas within speech recognition where the Wavelet Transform has been applied with some success. Examples include pitch detection, formant tracking, phoneme classification. Finally, our wavelet based feature extraction system is described and its performance on a simple phonetic classification problem given.

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Application of adaptive wavelets for speech coding

Cited by 13 publications

References 7 publications

Financial Transactions in ATM Machines using Speech Signals

Financial Transactions in ATM Machines using Speech Signals

Progressive Audio Scrambling in Compressed Domain

Wavelet based feature extraction for phoneme recognition

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