Computer science

With the recent upsurge in music consumption, music recommendation systems have gained substantial prominence. Platforms like Spotify are increasingly relied upon by users for curated music, underscoring the need for improved recommendation algorithms. While the analysis of user preferences and historical listening behaviours has conventionally been employed to tailor recommendations, these techniques are often restricted to examining textual data, such as lyrics and titles, thereby potentially limiting the effectiveness of the recommendations. The current study proposes a novel approach that extends beyond textual analysis to investigate the audio aspect of music, which directly influences listeners' emotions. This exploration encompasses the feature extraction and selection phases based on multi-models, contributing to robustness and interpretability, especially when contending with noise generated by the audio signal. Three distinct strategies for feature extraction and selection were incorporated, focusing on musical characteristics such as speed, rhythm, tonality, and signal changes. These strategies employed Librosa, PyAudio analyses, and Convolutional Neural Networks (CNNs) using the VGG16 model. Subsequently, features were classified to assess their efficacy and provide a preliminary evaluation of the proposed recommendation system. The system's personalisation was achieved by enabling users to select a piece of music, from which their preferences were extracted. The efficacy of this approach was validated through extensive experiments using the GTZAN dataset, comprising 10 distinct music genres with 100 audio files lasting 30 seconds each. Findings suggest that CNNs present a reliable method for generating personalised music recommendations, particularly for users with preferences for similar artists or diverse genres. Conversely, for users favouring a specific genre, Librosa appeared to provide a more effective means of achieving optimal recommendation accuracy. Therefore, this study illuminates new pathways for music analysis and classification, with the ultimate goal of enhancing understanding of the auditory world and improving the music recommendation experience for users.

Keyword spotting is the task of identifying the occurrences of certain desired keywords in an arbitrary speech signal. Keyword spotting has many applications one of them is telephone routing. In particular, we consider a big company which receives thousands of telephone calls daily. We are interested with the classification of these calls to route them to the appropriate department. State-of-the-art approaches in keyword spotting considered the speech signal as continuous speech, where keywords and non-keywords are modeled using hidden Markov models. In this paper, we suggest a perceptual approach, which exploit acoustic particularities of the predefined keywords to detect their frontiers, without modeling the Out-Of-Vocabulary (OOV) words.