AIMC Topic: Sound Spectrography

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Minimum Entropy Rate Simplification of Stochastic Processes.

IEEE transactions on pattern analysis and machine intelligence
We propose minimum entropy rate simplification (MERS), an information-theoretic, parameterization-independent framework for simplifying generative models of stochastic processes. Applications include improving model quality for sampling tasks by conc...

Characterizing Vocal Repertoires--Hard vs. Soft Classification Approaches.

PloS one
To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individua...

Human-inspired sound environment recognition system for assistive vehicles.

Journal of neural engineering
OBJECTIVE: The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct ...

Classification of sounds from Pacific white-sided dolphins using a convolutional neural network and a method to reduce false-positive detections.

The Journal of the Acoustical Society of America
An automatic detector for identifying the clicks and pulsed calls of Pacific white-sided dolphins (Lagenorhynchus obliquidens) was developed using a convolutional neural network architecture for passive acoustic monitoring, particularly in the areas ...

Characteristics of lung sounds in early infants using automated analysis.

European journal of pediatrics
UNLABELLED: A new lung sound analysis software program has been developed. It can automatically select a typical lung sound spectrogram and calculate lung sound parameters using machine learning programs. This study aimed to clarify lung sound charac...

Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices.

Journal of voice : official journal of the Voice Foundation
OBJECTIVE: To analyze the performance of 10 different machine learning (ML) classifiers for discrimination between dysphonic and non-dysphonic voices, using a variance threshold as a method for the selection and reduction of acoustic measurements use...

Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features.

Journal of voice : official journal of the Voice Foundation
Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection...

Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector machines.

The Journal of the Acoustical Society of America
Whales generate vocalizations which may, deliberately or not, encode caller identity cues. In this study, we analyze calls produced by Bryde's whales and recorded by ocean-bottom arrays of hydrophones deployed close to the Costa Rica Rift in the Pana...

The machine learning-based prediction of the sound pressure level from pathological and healthy speech signals.

The Journal of the Acoustical Society of America
Vocal intensity is quantified by sound pressure level (SPL). The SPL can be measured by either using a sound level meter or by comparing the energy of the recorded speech signal with the energy of the recorded calibration tone of a known SPL. Neither...

Rapid detection of fish calls within diverse coral reef soundscapes using a convolutional neural networka).

The Journal of the Acoustical Society of America
The quantity of passive acoustic data collected in marine environments is rapidly expanding; however, the software developments required to meaningfully process large volumes of soundscape data have lagged behind. A significant bottleneck in the anal...