Mood deep1/23/2024 Then, a Karhunen–Loeve transformation is applied to map the features to a rectangular space and eliminate information on random directions. To compute these features, first, we compute the Spectrogram of the audio signal and the result is fed to an octavian scale filter. Spectral Contrast features represent the intensity and contrast of spectral peaks and valleys. The mood classification from audio and the corresponding lyrics are the base problems that we faced efficiently, utilizing text and audio features. The general goal is to be able to develop a multi-modal emotion recognition system that will utilize text and audio concurrently and will be able to decide about the evoked emotion. Since we have access in certain media, such as lyrics (text) and audio, we will try to recognize what emotions these media can cause in a listener. However, we will mainly focus on the second clause of the definition. Emotion Recognition is the process that focuses on recognizing the emotion expressed by a human or identifying the emotion a medium (e.g., text, video, audio) can evoke to a human. Natural Language Processing and Deep Learning will be applied to our task in terms of Emotion Recognition (Mood Classification). In addition to listener features, music-related emotions also depend on other factors such as the influence of others or the environment music is listened to. Therefore it is reasonable to say that the background and experiences of individuals determine their perception and interpretation of music, although in most cases evoked emotions from listening to music have a global interpretation. Beyond age, a very important role in the perception of emotion from music have the cultural influences, as the study of Susino and Schubert suggests. The ability to perceive emotion from music is said to develop from the first years of human life and evolves over time. The contribution of our research work in the field of music mood detection can be summarized in three key points: (a) multi-modal approaches are way more effective than uni-modal (b) Transfer Learning and transformers can enhance the robustness of multi-modal systems and (c) the correct extraction and combination of audio features can further improve the prediction goal. Subsequently, recommended architectures and data representations analyzed, and the experimental process for training and evaluation described in detail. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data.
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