Music Emotion Recognition (MER) involves identifying the emotional content conveyed by music. This field is becoming increasingly significant due to its broad range of applications, including music recommendation systems, mood-based playlists, and therapeutic tools. This paper presents a novel MER model designed for song-level analysis, leveraging the Transformer Encoder architecture. The model incorporates various embedding techniques to capture both local and global contexts within musical data, thereby improving the extraction of crucial features for emotion recognition. Additionally, a Self-Attention Pooling Layer is used to effectively integrate and interpret complex musical features. Experiments using the DEAM dataset reveal that this model excels in emotion identification, surpassing existing approaches and offering promising directions for future research in the field of MER.

Speech-based automatic smoker identification (also known as smoker/non-smoker classification) aims to identify speakers’ smoking status from their speech. In the COVID-19 pandemic, speech-based automatic smoker identification approaches have received more attention in smoking cessation research due to low cost and contactless sample collection. This study focuses on determining the best acoustic features for smoker identification. In this paper, we investigate the performance of four acoustic feature sets/representations extracted using three feature extraction/learning approaches: (i) hand-crafted feature sets including the extended Geneva Minimalistic Acoustic Parameter Set and the Computational Paralinguistics Challenge Set, (ii) the Bag-of-Audio-Words representations, (iii) the neural representations extracted from raw waveform signals by SincNet. Experimental results show that: (i) SincNet feature representations are the most effective for smoker identification and outperform the MFCC baseline features by 16% in absolute accuracy; (ii) the performance of hand-crafted feature sets and the Bag-of-Audio-Words representations rely on the scale of the dimensions of feature vectors.