60 documents found, page 1 of 6
Active replay for continual learning in plant species classification
Costa, Dinis; Costa, Joana; Ribeiro, Bernardete; Sousa, José Paulo; Paiva, Rui Pedro; Silva, José Rafael; Lourenço, Rui; Silva, CatarinaContinual Learning (CL) is essential in dynamic environments, such as biodiversity monitoring, due to the continuous emergence of new species or changes in their distribution over time. A major challenge in CL is catastrophic forgetting, where a model loses performance on previously learned classes as it learns new ones. One common approach to mitigate this issue is replay, in which samples from previous tasks ...
Improving Music Emotion Recognition By Leveraging Handcrafted And Learned Features
Louro, Pedro Lima; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoMusic Emotion Recognition was dominated by classical machine learning, which relies on traditional classifiers and feature engineering (FE). Recently, deep learning approaches have been explored, aiming to remove the need for handcrafted features by automatic feature learning (FL), albeit at the expense of requiring large volumes of data to fully exploit their capabilities. A hybrid approach fusing information ...
MERGE App: A Prototype Software for Multi-User Emotion-Aware Music Management
Louro, Pedro Lima; Branco, Guilherme; Redinho, Hugo; Correia, Ricardo; Malheiro, Ricardo; Panda, Renato; Paiva, Rui PedroWe present a prototype software for multi-user music library management using the perceived emotional content of songs. The tool offers music playback features, song filtering by metadata, and automatic emotion prediction based on arousal and valence, with the possibility of personalizing the predictions by allowing each user to edit these values based on their own emotion assessment. This is an important featu...
Exploring Song Segmentation for Music Emotion Variation Detection
Ferreira, Tomás G.; Redinho, Hugo; Louro, Pedro Lima; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoThis paper evaluates the impact of song segmentation on Music Emotion Variation Detection (MEVD). In particular, the All-In-One song-structure segmentation system was employed to this end and compared to a fixed 1.5-sec window approach. Acoustic features were extracted for each obtained segment/window, which were classified with SVMs. The attained results (best F1-score of 55.9%) suggest that, despite its promi...
Exploring Deep Learning Methodologies for Music Emotion Recognition
Louro, Pedro Lima; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoThe increasingly globalized world we live in today and the wide availability of music at our fingertips have led to more diverse musical tastes within younger generations than in older generations. Moreover, these disparities are still not well understood, and the extent to which they affect listeners' preferences and perception of music. Focusing on the latter, this study explores the differences in emotional ...
Improving Deep Learning Methodologies for Music Emotion Recognition
Louro, Pedro Lima; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoMusic Emotion Recognition (MER) has traditionally relied on classical machine learning techniques. Progress on these techniques has plateaued due to the demanding process of crafting new, emotionally-relevant audio features. Recently, deep learning (DL) methods have surged in popularity within MER, due to their ability of automatically learning features from the input data. Nonetheless, these methods need large...
A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition
Louro, Pedro Lima; Redinho, Hugo; Malheiro, Ricardo Manuel da Silva; Paiva, Rui Pedro; Panda, RenatoClassical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such neces...
A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition
Louro, Pedro; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoClassical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such neces...
Improving Deep Learning Methodologies for Music Emotion Recognition
Louro, Pedro Lima; Redinho, Hugo; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoMusic Emotion Recognition (MER) has traditionally relied on classical machine learning techniques. Progress on these techniques has plateaued due to the demanding process of crafting new, emotionally-relevant audio features. Recently, deep learning (DL) methods have surged in popularity within MER, due to their ability of automatically learning features from the input data. Nonetheless, these methods need large...
Exploring Song Segmentation for Music Emotion Variation Detection
Ferreira, Tomas; Redinho, Hugo; Louro, Pedro L.; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, RenatoThis paper evaluates the impact of song segmentation on Music Emotion Variation Detection (MEVD). In particular, the All-In-One song-structure segmentation system was employed to this end and compared to a fixed 1.5-sec window approach. Acoustic features were extracted for each obtained segment/window, which were classified with SVMs. The attained results (best F1-score of 55.9%) suggest that, despite its promi...