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    Wyświetlanie 1-3 z 3
    Tytuł:
    Music Playlist Generation using Facial Expression Analysis and Task Extraction
    Autorzy:
    Sen, A.
    Popat, D.
    Shah, H.
    Kuwor, P.
    Johri, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/908868.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    facial expression analysis
    emotion recognition
    feature extraction
    viola jones face detection
    gabor filter
    adaboost
    k-NN algorithm
    task extraction
    music classification
    playlist generation
    Opis:
    In day to day stressful environment of IT Industry, there is a truancy for the appropriate relaxation time for all working professionals. To keep a person stress free, various technical or non-technical stress releasing methods are now being adopted. We can categorize the people working on computers as administrators, programmers, etc. each of whom require varied ways in order to ease themselves. The work pressure and the vexation of any kind for a person can be depicted by their emotions. Facial expressions are the key to analyze the current psychology of the person. In this paper, we discuss a user intuitive smart music player. This player will capture the facial expressions of a person working on the computer and identify the current emotion. Intuitively the music will be played for the user to relax them. The music player will take into account the foreground processes which the person is executing on the computer. Since various sort of music is available to boost one's enthusiasm, taking into consideration the tasks executed on the system by the user and the current emotions they carry, an ideal playlist of songs will be created and played for the person. The person can browse the playlist and modify it to make the system more flexible. This music player will thus allow the working professionals to stay relaxed in spite of their workloads.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2016, 16, 2; 1-6
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks
    Autorzy:
    Meng, Hao
    Yan, Tianhao
    Wei, Hongwei
    Ji, Xun
    Powiązania:
    https://bibliotekanauki.pl/articles/2173587.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    voice activity detection
    wavelet packet reconstruction
    feature extraction
    LSTM networks
    attention mechanism
    rozpoznawanie emocji mowy
    wykrywanie aktywności głosowej
    rekonstrukcja pakietu falkowego
    wyodrębnianie cech
    mechanizm uwagi
    sieć LSTM
    Opis:
    Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; art. no. e136300
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks
    Autorzy:
    Meng, Hao
    Yan, Tianhao
    Wei, Hongwei
    Ji, Xun
    Powiązania:
    https://bibliotekanauki.pl/articles/2090711.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    voice activity detection
    wavelet packet reconstruction
    feature extraction
    LSTM networks
    attention mechanism
    rozpoznawanie emocji mowy
    wykrywanie aktywności głosowej
    rekonstrukcja pakietu falkowego
    wyodrębnianie cech
    mechanizm uwagi
    sieć LSTM
    Opis:
    Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; e136300, 1--12
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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