Laboratorium Akustyki Fonicznej - Administrative Units - MOST Wiedzy

Search

Laboratorium Akustyki Fonicznej

Filters

total: 340

  • Category
  • Year
  • Options

clear all filters disabled

Catalog Publications

Year 2021
Year 2020
  • A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces
    Publication
    • G. Tamulevicius
    • G. Korvel
    • A. Yayak
    • P. Treigys
    • J. Bernataviciene
    • B. Kostek

    - Electronics - Year 2020

    In this research, a study of cross-linguistic speech emotion recognition is performed. For this purpose, emotional data of different languages (English, Lithuanian, German, Spanish, Serbian, and Polish) are collected, resulting in a cross-linguistic speech emotion dataset with the size of more than 10.000 emotional utterances. Despite the bi-modal character of the databases gathered, our focus is on the acoustic representation...

    Full text available to download

  • Analiza ruchu drogowego z wykorzystaniem analizy akustycznej

    Tematyka pracy porusza zagadnienia dotyczące pozyskiwania informacji o ruchu drogowym z wykorzystaniem monitoringu akustycznego. Przybliżono podstawowe techniki nadzoru nad ruchem drogowym. Przedstawiono założenia akustycznego detektora ruchu i zbadano jego skuteczność na trzech płaszczyznach działania – zliczania pojazdów, klasyfikacji rodzajowej i klasyfikacji warunków pogodowych panujących na nawierzchni

    Full text to download in external service

  • Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
    Publication

    The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...

    Full text to download in external service

  • Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
    Publication

    The purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....

    Full text available to download

  • Evaluation of Lombard Speech Models in the Context of Speech in Noise Enhancement
    Publication

    - IEEE Access - Year 2020

    The Lombard effect is one of the most well-known effects of noise on speech production. Speech with the Lombard effect is more easily recognizable in noisy environments than normal natural speech. Our previous investigations showed that speech synthesis models might retain Lombard-effect characteristics. In this study, we investigate several speech models, such as harmonic, source-filter, and sinusoidal, applied to Lombard speech...

    Full text available to download

  • Improving Objective Speech Quality Indicators in Noise Conditions
    Publication

    - Year 2020

    This work aims at modifying speech signal samples and test them with objective speech quality indicators after mixing the original signals with noise or with an interfering signal. Modifications that are applied to the signal are related to the Lombard speech characteristics, i.e., pitch shifting, utterance duration changes, vocal tract scaling, manipulation of formants. A set of words and sentences in Polish, recorded in silence,...

    Full text to download in external service

  • Investigating Feature Spaces for Isolated Word Recognition
    Publication
    • P. Treigys
    • G. Korvel
    • G. Tamulevicius
    • J. Bernataviciene
    • B. Kostek

    - Year 2020

    The study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...

    Full text to download in external service

  • Musical Instrument Tagging Using Data Augmentation and Effective Noisy Data Processing

    Developing signal processing methods to extract information automatically has potential in several applications, for example searching for multimedia based on its audio content, making context-aware mobile applications (e.g., tuning apps), or pre-processing for an automatic mixing system. However, the last-mentioned application needs a significant amount of research to reliably recognize real musical instruments in recordings....

    Full text available to download

  • Ranking Speech Features for Their Usage in Singing Emotion Classification
    Publication

    - Year 2020

    This paper aims to retrieve speech descriptors that may be useful for the classification of emotions in singing. For this purpose, Mel Frequency Cepstral Coefficients (MFCC) and selected Low-Level MPEG 7 descriptors were calculated based on the RAVDESS dataset. The database contains recordings of emotional speech and singing of professional actors presenting six different emotions. Employing the algorithm of Feature Selection based...

    Full text to download in external service

Year 2019
Year 2018