Non-speech acoustic events are significantly different between them, and usually require access to detail rich features. That is why directly modeling a real spectrogram can provide a significant advantage, instead of using predefined features that usually compress and downsample detail as typically done in speech recognition. This paper focuses on the importance of feature extraction for deep ...

This work explores the use of constant-Q transform based modulation spectral features (CQT-MSF) for speech emotion recognition (SER). The human perception and analysis sound comprise two important cognitive parts: early auditory cortex-based processing. considers spectrogram-based representation whereas includes extraction temporal modulations from spectrogram. spectrogram is called feature (MS...

When convolutional neural networks are used to tackle learning problems based on music or, more generally, time series data, raw one-dimensional data are commonly pre-processed to obtain spectrogram or mel-spectrogram coefficients, which are then used as input to the actual neural network. In this contribution, we investigate, both theoretically and experimentally, the influence of this pre-pro...

INTRODUCTION Perceptual evaluation of voice quality remains a key standard for judgment of vocal impairment. The GRABS method has become a commonly-used scale for rating severity of dysphonia, but it has no published, standardised protocol to follow. Training is important for reaching good interrater agreement for its parameters; however, the references most often cited for the GRABS provide no...

Starting with a novel audio analysis and editing paradigm, a set of new and adaptive audio analysis and editing algorithms in the spectrogram are developed and integrated into a smart visual audio editing tool in a “what you see is what you hear” style. At the core of our algorithms and methods is a very flexible audio spectrogram that goes beyond FFT and Wavelets and supports manipulating a si...

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea-hypopnea index were the targets o...

The acoustic space in a given environment is filled with footprints arising from three processes: biophony, geophony and anthrophony. Bioacoustic research using passive acoustic sensors can result in thousands of recordings. An important component of processing these recordings is to automate signal detection. In this paper, we describe a new spectrogram-based approach for extracting individual...

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral informatio...