Speech and Audio Signal Applications

2005 ◽  
pp. 2592-2596
Author(s):  
Hector Perez-Meana ◽  
Mariko Nakano-Miyatake
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Audio Signals ◽  
Vlsi Technology ◽  
Transmission Enhancement

With the development of the VLSI technology the performance of signal processing devices have greatly improved making possible the implementation of more efficient systems to storage, transmission enhancement and reproduction of speech and audio signals. Some of these successful applications are shown in Table 1.

Signal Processing Techniques for Audio and Speech Applications

2011 ◽  
pp. 3457-3461
Author(s):  
Hector Perez-Meana ◽  
Mariko Nakano-Miyatake
Keyword(s):  
Signal Processing ◽  
Copyright Protection ◽  
Digital Signal Processor ◽  
Digital Signal ◽  
Medical Systems ◽  
Audio Signals ◽  
Vlsi Technology ◽  
Transmission Enhancement ◽  
Processing Techniques ◽  
Signal Processing Techniques

Since the apparition of the first standalone digital signal processor (DSP) in 1980, the development of very-largescale integration (VLSI) technology has allowed an impressive improvement on the performance of signal processing devices. This fact has made it possible to implement more efficient systems for storage, transmission, enhancement, protection, and reproduction of speech and audio signals. Some of these successful applications, shown in Table 1, have contributed to improving the performance of communications, storage, and medical systems, as well as security and copyright protection.

Spectral-Based Analysis and Synthesis of Audio Signals

2011 ◽  
pp. 56-92 ◽  
Author(s):  
Paulo A.A. Esquef ◽  
Luiz W.P. Biscainho
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Parametric Representation ◽  
Sound Generation ◽  
Audio Signal Processing ◽  
Audio Signals ◽  
Sinusoidal Modeling ◽  
Analysis And Synthesis ◽  
Processing Techniques ◽  
Signal Processing Techniques

This chapter reviews audio signal processing techniques related to sound generation via additive synthesis. Particular focus will be put on sinusoidal modeling. Each processing stage involved in obtaining a sinusoidal representation for audio signals is described. Then, synthesis techniques that allow reconstructing an audio signal based on a given parametric representation are presented. Finally, some audio applications where sinusoidal modeling is employed are briefly discussed.

scholarly journals An Effective Watermarking Method Based on Energy Averaging in Audio Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
S. E. Tsai ◽  
S. M. Yang
Keyword(s):  
Signal Processing ◽  
Data Compression ◽  
Error Correcting Code ◽  
Audio Signal ◽  
Segment Length ◽  
Signal Quality ◽  
Audio Signals ◽  
Segment Sequence ◽  
Audio Quality ◽  
Dct Coefficients

Methods based on discrete cosine transform (DCT) have been proposed for digital watermarking of audio signals; however, the watermark is often vulnerable to data compression and signal processing. This paper presents an effective audio watermarking method by energy averaging of DCT coefficients such that an audio signal with watermark is robust to data processing. The method is to divide an audio signal into segments by three parameters defining the segment length, the segment sequence of watermark location, and the frequency range of DCT coefficients for watermark location. An error correcting code is also integrated to improve audio signal quality after watermarking. Experimental results show that the method is robust to data compression and many other kinds of signal processing. No original signal is required for decoding the watermark. Comparison of watermarking performance with a recent work validates that the watermarking method has better audio quality and higher robustness.

scholarly journals Audiovisual Tool for understanding Audio concepts for being used in bachelor’s degree programmes

2016 ◽  
Author(s):  
Jose A. Belloch ◽  
Christian Antoñanzas ◽  
Pablo Gutierrez-Parera ◽  
Mª Angeles Simarro
Keyword(s):  
Signal Processing ◽  
Audio Signal ◽  
Bachelor's Degree ◽  
Audio Signal Processing ◽  
Audio Signals ◽  
Academic Courses ◽  
Practical Session

In the Audio Signal Processing field, there exists difficulties in order to explain different concepts such as, compression, masking, quantization, sampling, among others. Further, most of these concepts require the use of audio laboratories and multiple practical session that must carry out students. Another issue is that there are students that are not able to internalize these concepts straightforwardly and require more practical sessions. In order to address these problems, we have developed an audiovisual tool, designed with Matlab, that can be used for professors and students. This tool allows to analyze, test and apply the audio concepts to real audio signals. The developed tool has been successfully experienced by professors of the audio signal processing field that recommend its use in upcoming academic courses.

scholarly journals A Review on Machine Learning for Audio Applications

2021 ◽  
Vol 23 (07) ◽  
pp. 62-70
Author(s):  
Nagesh B ◽  
◽  
Dr. M. Uttara Kumari ◽  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Language Processing ◽  
Speech Processing ◽  
Noise Suppression ◽  
Audio Signal ◽  
Audio Signals ◽  
Audio Processing ◽  
Speech Generation

Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.

scholarly journals The in-service determination of the presence of distortion in a high quality analogue sound signal

2007 ◽  
Author(s):  
◽  
Stefanus Mare
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Audio Signal ◽  
Digital Signal ◽  
Audio Signals ◽  
High Quality ◽  
Number Of Factors ◽  
Non Stationary Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

Detecting and minimising distortion in audio signals is an important aspect of sound engineering. Distortion of a signal passing through an audio system may be caused by a number of factors and it is necessary to detect these effects for optimal sound. The problem is of interest to users and operators of high quality audio equipment and transmission facilities. The objective of this thesis was the development of techniques for the blind identification of distortion in a high quality audio signal using digital signal processing techniques. The techniques developed are based on digital signal processing techniques and statistical analysis of a recorded audio signal, which is treated as a random, non-stationary signal.

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

scholarly journals Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1349
Author(s):  
Stefan Lattner ◽  
Javier Nistal
Keyword(s):  
Data Storage ◽  
Audio Signal ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Audio Signals ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Extensive Evaluation ◽  
Listening Tests ◽  
Musical Audio

Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs may introduce a variety of impairments in the audio signal. Many works have tackled the problem of audio enhancement and compression artifact removal using deep-learning techniques. However, only a few works tackle the restoration of heavily compressed audio signals in the musical domain. In such a scenario, there is no unique solution for the restoration of the original signal. Therefore, in this study, we test a stochastic generator of a Generative Adversarial Network (GAN) architecture for this task. Such a stochastic generator, conditioned on highly compressed musical audio signals, could one day generate outputs indistinguishable from high-quality releases. Therefore, the present study may yield insights into more efficient musical data storage and transmission. We train stochastic and deterministic generators on MP3-compressed audio signals with 16, 32, and 64 kbit/s. We perform an extensive evaluation of the different experiments utilizing objective metrics and listening tests. We find that the models can improve the quality of the audio signals over the MP3 versions for 16 and 32 kbit/s and that the stochastic generators are capable of generating outputs that are closer to the original signals than those of the deterministic generators.

Sign in / Sign up

Export Citation Format

Share Document