How Large Is an Audio Signal?

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.

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Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

Applied Sciences ◽

10.3390/app11146288 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6288

Author(s):

Hang Su ◽

Chang-Myung Lee

Keyword(s):

Speech Signal ◽

Output Signal ◽

Cross Correlation ◽

Acoustic Noise ◽

Audio Signal ◽

Noise Signal ◽

Lms Algorithm ◽

Least Mean Square ◽

Experiment Data ◽

Noise Component

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

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Creation of Auditory Augmented Reality Using a Position-Dynamic Binaural Synthesis System—Technical Components, Psychoacoustic Needs, and Perceptual Evaluation

Applied Sciences ◽

10.3390/app11031150 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1150

Author(s):

Stephan Werner ◽

Florian Klein ◽

Annika Neidhardt ◽

Ulrike Sloma ◽

Christian Schneiderwind ◽

...

Keyword(s):

Augmented Reality ◽

Auditory Perception ◽

Audio Signal ◽

Spatial Audio ◽

Impulse Responses ◽

Synthesis System ◽

Perceptual Evaluation ◽

Listening Tests ◽

Work Done ◽

Audio Reproduction

For a spatial audio reproduction in the context of augmented reality, a position-dynamic binaural synthesis system can be used to synthesize the ear signals for a moving listener. The goal is the fusion of the auditory perception of the virtual audio objects with the real listening environment. Such a system has several components, each of which help to enable a plausible auditory simulation. For each possible position of the listener in the room, a set of binaural room impulse responses (BRIRs) congruent with the expected auditory environment is required to avoid room divergence effects. Adequate and efficient approaches are methods to synthesize new BRIRs using very few measurements of the listening room. The required spatial resolution of the BRIR positions can be estimated by spatial auditory perception thresholds. Retrieving and processing the tracking data of the listener’s head-pose and position as well as convolving BRIRs with an audio signal needs to be done in real-time. This contribution presents work done by the authors including several technical components of such a system in detail. It shows how the single components are affected by psychoacoustics. Furthermore, the paper also discusses the perceptive effect by means of listening tests demonstrating the appropriateness of the approaches.

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402 Audio information retrieval for describing gait patterns in Brazilian horses

Journal of Animal Science ◽

10.1093/jas/skaa278.048 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 27-27

Author(s):

Ricardo V Ventura ◽

Rafael Z Lopes ◽

Lucas T Andrietta ◽

Fernando Bussiman ◽

Julio Balieiro ◽

...

Keyword(s):

Information Retrieval ◽

Subjective Evaluation ◽

Audio Signal ◽

Principal Component ◽

Potential Method ◽

Economic Sectors ◽

Audio Features ◽

Horse Industry ◽

Audio Files ◽

Audio Information

Abstract The Brazilian gaited horse industry is growing steadily, even after a recession period that affected different economic sectors in the whole country. Recent numbers suggested an increase on the exports, which reveals the relevance of this horse market segment. Horses are classified according to the gait criteria, which divide the horses in two groups associated with the animal movements: lateral (Marcha Picada) or diagonal (Marcha_Batida). These two gait groups usually show remarkable differences related to speed and number of steps per fixed unit of time, among other factors. Audio retrieval refers to the process of information extraction obtained from audio signals. This new data analysis area, in comparison to traditional methods to evaluate and classify gait types (as, for example, human subjective evaluation and video monitoring), provides a potential method to collect phenotypes in a reduced cost manner. Audio files (n = 80) were obtained after extracting audio features from freely available YouTube videos. Videos were manually labeled according to the two gait groups (Marcha Picada or Marcha Batida) and thirty animals were used after a quality control filter step. This study aimed to investigate different metrics associated with audio signal processing, in order to first cluster animals according to the gait type and subsequently include additional traits that could be useful to improve accuracy during the identification of genetically superior animals. Twenty-eight metrics, based on frequency or physical audio aspects, were carried out individually or in groups of relative importance to perform Principal Component Analysis (PCA), as well as to describe the two gait types. The PCA results indicated that over 87% of the animals were correctly clustered. Challenges regarding environmental interferences and noises must be further investigated. These first findings suggest that audio information retrieval could potentially be implemented in animal breeding programs, aiming to improve horse gait.

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Fast motion estimation of one-dimensional laser speckle image and its application on real-time audio signal acquisition

2020 the 6th International Conference on Communication and Information Processing ◽

10.1145/3442555.3442576 ◽

2020 ◽

Author(s):

Nan Wu ◽

Shinichiro Haruyama

Keyword(s):

Motion Estimation ◽

Real Time ◽

Audio Signal ◽

Laser Speckle ◽

Signal Acquisition ◽

Fast Motion Estimation ◽

One Dimensional ◽

Speckle Image ◽

Fast Motion

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

Sensors ◽

10.3390/s21030676 ◽

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.

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Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Electronics ◽

10.3390/electronics10111349 ◽

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.

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