scholarly journals Dynamics of the formation of a differentiation track conditioned reflex to sound stimuli of a certain duration in the gray seal Halichoerus grypus Fabricius, 1791 by the methods of operant conditioning.

2021 ◽  
Vol 12 (3-2021) ◽  
pp. 7-13
Author(s):  
A.F. Berdnik ◽  
Keyword(s):  
Reaction Time ◽  
Operant Conditioning ◽  
Conditioned Reflex ◽  
Audio Signal ◽  
Halichoerus Grypus ◽  
Audio Signals ◽  
Gray Seal ◽  
And Behavior ◽  
Sound Duration

In the course of the study, a 15-year-old female gray seal was trained to press a button after displaying an audio signal for 5 seconds and ignore similar audio signals of longer or shorter duration. The conducted research has demonstrated the ability of the experimental seal to reliably differentiate sound signals with a difference in sound duration of 3 seconds. Changes in the reaction time and behavior of the seal during the demonstration of sound stimuli with distinguishable and indistinguishable time ranges are described.

scholarly journals Daily and seasonal movements of Cape Cod gray seals vary with predation risk

2020 ◽  
Vol 644 ◽  
pp. 215-228 ◽  
Author(s):  
JH Moxley ◽  
G Skomal ◽  
J Chisholm ◽  
P Halpin ◽  
DW Johnston
Keyword(s):  
Predation Risk ◽  
White Shark ◽  
Halichoerus Grypus ◽  
Cape Cod ◽  
Gray Seal ◽  
Predator Prey ◽  
Seasonal Movements ◽  
Nocturnal Foraging ◽  
And Behavior ◽  
Gray Seals

White sharks Carcharodon carcharias and gray seals Halichoerus grypus are re-establishing their ecological roles within the Northwestern Atlantic Ocean, presenting an opportunity to understand gray seal movement and at-sea behavior under predation risk. As with other shark-seal hotspots, movements to and from terrestrial haul outs can be risky for gray seals, thereby eliciting antipredator strategies. We investigated the movement and coastal behavior of gray seals on Cape Cod (USA) in relation to seasonal and diel changes in white shark activity. Analyzing 412 trips to sea by 8 seals and more than 25000 acoustic detections from 23 individual white sharks, we observed seasonally homogeneous movements in seal behavior during months with greater shark presence. During riskier months, seal behavior manifested in near-exclusive nocturnal foraging, reduced offshore ranging, and limited at-sea activity. On these nocturnal trips to sea, seals returning to haul outs tended to avoid daybreak and traversed during diel minima in shark activity. However, seals tended to depart haul outs at dusk when shark presence was maximal. As conservation efforts succeed in rebuilding depleted populations of coastal predators, studying re-emerging predator-prey interactions can enhance our understanding about the drivers of movement and behavior.

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.

scholarly journals Layered multiple description coding of multi-channel audio signals

2021 ◽  
Author(s):  
Katarina Stojadinović
Keyword(s):  
Audio Signal ◽  
Multiple Description Coding ◽  
Packet Networks ◽  
Audio Signals ◽  
Packet Losses ◽  
Loss Mitigation ◽  
Efficient Coding ◽  
Multiple Description ◽  
Redundancy Coding ◽  
Description Coding

In this study, we investigate efficient coding of multi-channel audio signals for transmission over packet networks. The techniques studied and developed as part of this research are based on redundancy coding and aim to achieve robustness with respect to packet losses. The resulting algorithm also addresses the needs of network clients with varying access bandwidths; the algorithm generates multi-layer encoded data streams which can range from basic mono to full multi-channel surround audio. Loss mitigation is achieved by applying multiple description coding technique based on the priority encoding transmission packetization scheme. The hierarchy of the transmitted data is derived from a statistical analysis of the multi-channel audio signal. Inter-channel correlations form the basis for estimating the multi-channel audio signal form the received descriptions at the decoder.

scholarly journals Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

2021 ◽  
Author(s):  
Shahrzad Esmaili
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Data Privacy ◽  
Audio Signal ◽  
Maximum Energy ◽  
Audio Watermarking ◽  
Audio Signals ◽  
Time Frequency Analysis ◽  
Linear Discriminant ◽  
Time Frequency

This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.

scholarly journals ANALYSIS AND IMPROVISATION IN EXTRACTING AUDIO SIGNAL AMPLITUDE USING LABVIEW

2020 ◽  
pp. 273-278
Author(s):  
Adarsh V Srinivasan ◽  
Mr. N. Saritakumar
Keyword(s):  
Virtual Instrument ◽  
Sampling Rate ◽  
Audio Signal ◽  
Signal Amplitude ◽  
Audio Signals ◽  
Virtual Instrumentation ◽  
Audio Processing ◽  
Reading And Writing ◽  
Labview Software ◽  
Frequency Sampling

In this paper, either a pre-recorded audio or a newly recorded audio is processed and analysed using the LabVIEW Software by National Instruments. All the data such as bitrate, number of channels, frequency, sampling rate of the Audio are analyzed and improvising the signal by a few operations like Amplification, De-Amplification, Inversion and Interlacing of Audio Signals are done. In LabVIEW, there are a few Sub Virtual Instrument’s available for Reading and Writing Audio in .wav formats and using them and array Sub Virtual Instrument, all the processing are done. KEYWORDS: Virtual Instrumentation (VI), LabVIEW (LV), Audio, Processing, audio array.

Data Hiding for Stereo Audio Signals

2013 ◽  
pp. 104-128
Author(s):  
Kazuhiro Kondo
Keyword(s):  
Data Hiding ◽  
Audio Signal ◽  
Audio Coding ◽  
Original Signal ◽  
Audio Signals ◽  
Audio Quality ◽  
Input Source ◽  
Rate Conversion ◽  
Fixed Delay

This chapter proposes two data-hiding algorithms for stereo audio signals. The first algorithm embeds data into a stereo audio signal by adding data-dependent mutual delays to the host stereo audio signal. The second algorithm adds fixed delay echoes with polarities that are data dependent and amplitudes that are adjusted such that the interchannel correlation matches the original signal. The robustness and the quality of the data-embedded audio will be given and compared for both algorithms. Both algorithms were shown to be fairly robust against common distortions, such as added noise, audio coding, and sample rate conversion. The embedded audio quality was shown to be “fair” to “good” for the first algorithm and “good” to “excellent” for the second algorithm, depending on the input source.

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.

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.

Systematic Errors Occur in the Discrimination of Complex Audio Signals

1992 ◽  
Vol 36 (3) ◽  
pp. 263-267
Author(s):  
Jeffrey M. Gerth
Keyword(s):  
Duty Cycle ◽  
Temporal Pattern ◽  
Temporal Patterns ◽  
Systematic Errors ◽  
Second Sound ◽  
Audio Signals ◽  
Complex Sounds ◽  
Number Of Components ◽  
Sound Category ◽  
Sound Duration

Previous research suggests that the temporal pattern of dissimilar sounds may be a basis for confusion. To extend this research, the present study used complex sounds formed by simultaneously playing components drawn from four sound categories. Four temporal patterns, determined by sound duration and duty cycle were also used, producing a total of 16 basic components. The density (i.e., number of components played simultaneously) ranged from one to four. Subjects heard a sequence of two complex sounds and judged whether they were same of different. For trials in which the sounds differed, there were three possible manipulations: the addition of a component, the deletion of a component, and the substitution of one component for another. Overall accuracy was 94 percent across the 144 dissimilar sound complexes. As density increased, a significantly greater number of errors occurred for all classes of manipulations. Changes in individual temporal patterns across a variety of manipulations of sounds involving adding, deleting and substituting components were accurately discriminated. Subjects were least accurate in detecting substitutions of a pattern. A single sound category was identified in error prone sequences which was most often involved as the changing component from first to second sound presentation. Suggestions for the design of easily discriminated sounds are discussed.

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