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Catalog Publications

Year 2023
  • Automatic music signal mixing system based on one-dimensional Wave-U-Net autoencoders
    Publication

    The purpose of this paper is to show a music mixing system that is capable of automatically mixing separate raw recordings with good quality regardless of the music genre. This work recalls selected methods for automatic audio mixing first. Then, a novel deep model based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. The model is trained on a custom-prepared database. Mixes created using the...

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  • Detecting Lombard Speech Using Deep Learning Approach
    Publication
    • K. Kąkol
    • G. Korvel
    • G. Tamulevicius
    • B. Kostek

    - SENSORS - Year 2023

    Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...

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Year 2022
Year 2021
  • Acoustic Sensing Analytics Applied to Speech in Reverberation Conditions
    Publication

    The paper aims to discuss a case study of sensing analytics and technology in acoustics when applied to reverberation conditions. Reverberation is one of the issues that makes speech in indoor spaces challenging to understand. This problem is particularly critical in large spaces with few absorbing or diffusing surfaces. One of the natural remedies to improve speech intelligibility in such conditions may be achieved through speaking...

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  • AUTOMATYCZNE GENEROWANIE KOLEJNOŚCI LIST UTWORÓW MUZYCZNYCH
    Publication

    - Year 2021

    W niniejszym rozdziale przedstawiono przygotowanie algorytmu do automa-tycznego układania kolejności utworów muzycznych i zgrywającego je do postaci jednego, długiego miksu. Dzięki algorytmowi dobierane są utwory na podstawie analizy podobieństwa fragmentów końcowych i początkowych utworów. Podo-bieństwo to jest obliczane za pomocą odległości euklidesowej między wektorami parametrów wyznaczonymi przez autoenkoder oraz na podstawie...

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  • Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
    Publication

    - Year 2021

    This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS). In a classical approach, audio features are usually extracted from fixed regions of speech such as the syllable nucleus. We propose an attention-based deep learning model that automatically de...

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  • Evaluation of aspiration problems in L2 English pronunciation employing machine learning

    The approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones...

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  • Evaluation of Six Degrees of Freedom 3D Audio Orchestra Recording and Playback using multi-point Ambisonic interpolation
    Publication
    • T. Ciotucha
    • A. Rumiński
    • T. Żernicki
    • B. Mróz

    - Scopus - Year 2021

    This paper describes a strategy for recording sound and enabling six-degrees-of-freedom playback, making use of multiple simultaneous and synchronized Higher Order Ambisonics (HOA) recordings. Such a strategy enables users to navigate in a simulated 3D space and listen to the six-degrees-of-freedom recordings from different perspectives. For the evaluation of the proposed approach, an Unreal Engine-based navigable 3D audiovisual...

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  • How Machine Learning Contributes to Solve Acoustical Problems
    Publication
    • M. A. Roch
    • P. Gerstoft
    • B. Kostek
    • Z. Michalopoulou

    - Journal of the Acoustical Society of America - Year 2021

    Machine learning is the process of learning functional relationships between measured signals (called percepts in the artificial intelligence literature) and some output of interest. In some cases, we wish to learn very specific relationships from signals such as identifying the language of a speaker (e.g. Zissman, 1996) which has direct applications such as in call center routing or performing a music information retrieval task...

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  • Introduction to the special issue on machine learning in acoustics
    Publication
    • Z. Michalopoulou
    • P. Gerstoft
    • B. Kostek
    • M. A. Roch

    - Journal of the Acoustical Society of America - Year 2021

    When we started our Call for Papers for a Special Issue on “Machine Learning in Acoustics” in the Journal of the Acoustical Society of America, our ambition was to invite papers in which machine learning was applied to all acoustics areas. They were listed, but not limited to, as follows: • Music and synthesis analysis • Music sentiment analysis • Music perception • Intelligent music recognition • Musical source separation • Singing...

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  • Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction
    Publication

    The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis...

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  • Mispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling
    Publication

    - Year 2021

    A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker. This approach makes two simplifying assumptions: a) phonemes can be recognized from speech with high accuracy, b) there is a single correct way for a sentence to be pronounced. These assumptions do not always hold, which can result...

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  • Reinforcement Learning Algorithm and FDTD-based Simulation Applied to Schroeder Diffuser Design Optimization
    Publication

    The aim of this paper is to propose a novel approach to the algorithmic design of Schroeder acoustic diffusers employing a deep learning optimization algorithm and a fitness function based on a computer simulation of the propagation of acoustic waves. The deep learning method employed for the research is a deep policy gradient algorithm. It is used as a tool for carrying out a sequential optimization process the goal of which is...

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  • Skuteczność klasyfikacji gatunków muzycznych za pomocą sieci neuronowej w zależności od typu danych wejściowych
    Publication

    Rozpoznawanie gatunku muzycznego jest jednym z podstawowych elementów inteligentnych systemów tworzenia automatycznych list muzyki. Platformy strumieniowe oferujące taką usługę wymagają rozwiązań, które umożliwią jak najdokładniej określić przynależność utworu do gatunku muzycznego. Zgodnie z aktualnym stanem wiedzy – najskuteczniejszym klasyfikatorem są sztuczne sieci neuronowe (w tym w wersji uczenia głębokiego), dla których...

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Year 2017
  • Sound intensity distribution around organ pipe

    The aim of the paper was to compare acoustic field around the open and stopped organ pipes. The wooden organ pipe was located in the anechoic chamber and activated with a constant air flow, produced by an external air-compressor. Thus, long-term steady state response was possible to obtain. Multichannel acoustic vector sensor was used to measure the sound intensity distribution of radiated acoustic energy. Measurements have been...

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