Loading...
18 results
Search Results
Now showing 1 - 10 of 18
- Envisaging a global infrastructure to exploit the potential of digitised collectionsPublication . Groom, Quentin; Dillen, Mathias; Addink, Wouter; Ariño, Arturo H.; Bölling, Christian; Bonnet, Pierre; Cecchi, Lorenzo; Ellwood, Elizabeth R.; Figueira, Rui; Gagnier, Pierre-Yves; Grace, Olwen; Güntsch, Anton; Hardy, Helen; Huybrechts, Pieter; Hyam, Roger; Joly, Alexis; Kommineni, Vamsi Krishna; Larridon, Isabel; Livermore, Laurence; Lopes, Ricardo Jorge; Meeus, Sofie; Miller, Jeremy; Milleville, Kenzo; Panda, Renato; Pignal, Marc; Poelen, Jorrit; Ristevski, Blagoj; Robertson, Tim; Rufino, Ana C.; Santos, Joaquim; Schermer, Maarten; Scott, Ben; Seltmann, Katja; Teixeira, Heliana; Trekels, Maarten; Gaikwad, JitendraTens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.
- How Does the Spotify API Compare to the Music Emotion Recognition State-of-the-Art?Publication . Panda, Renato; Redinho, Hugo; Gonçalves, Carolina; Malheiro, Ricardo; Paiva, Rui PedroFeatures are arguably the key factor to any machine learning problem. Over the decades, myriads of audio features and recently feature-learning approaches have been tested in Music Emotion Recognition (MER) with scarce improvements. Here, we shed some light on the suitability of the audio features provided by the Spotify API, the leading music streaming service, when applied to MER. To this end, 12 Spotify API features were obtained for 704 of our 900-song dataset, annotated in terms of Russell’s quadrants. These are compared to emotionally-relevant features obtained previously, using feature ranking and emotion classification experiments. We verified that energy, valence and acousticness features from Spotify are highly relevant to MER. However, the 12-feature set is unable to meet the performance of the features available in the state-of-the-art (58.5% vs. 74.7% F1-measure). Combining Spotify and state-of-the-art sets leads to small improvements with fewer features (top5: +2.3%, top10: +1.1%), while not improving the maximum results (100 features). From this we conclude that Spotify provides some higher-level emotionally-relevant features. Such extractors are desirable, since they are closer to human concepts and allow for interpretable rules to be extracted (harder with hundreds of abstract features). Still, additional emotionally-relevant features are needed to improve MER.
- Usability of a telehealth solution based on TV interaction for the elderly: the VITASENIOR-MT case studyPublication . Pires, Gabriel; Lopes, Ana; Correia, Pedro; Almeida, Luis; Oliveira, Luís; Panda, Renato; Jorge, Dario; Mendes, Diogo; Dias, Pedro; Gomes, Nelson; Pereira, TelmoRemote monitoring of biometric data in the elderly population is an important asset for improving the quality of life and level of independence of elderly people living alone. However, the design and implementation of health technological solutions often disregard the elderly physiological and psychological abilities, leading to low adoption of these technologies. We evaluate the usability of a remote patient monitoring solution, VITASENIOR-MT, which is based on the interaction with a television set. Twenty senior participants (over 64 years) and a control group of 20 participants underwent systematic tests with the health platform and assessed its usability through several questionnaires. Elderly participants scored high on the usability of the platform, very close to the evaluation of the control group. Sensory, motor and cognitive limitations were the issues that most contributed to the difference in usability assessment between the elderly group and the control group. The solution showed high usability and acceptance regardless of age, digital literacy, education and impairments (sensory, motor and cognitive), which shows its effective viability for use and implementation as a consumer product in the senior market.
- A Pattern Recognition Framework to Investigate the Neural Correlates of MusicPublication . Guedes, Ana Gabriela; Sayal, Alexandre; Panda, Renato; Paiva, Rui Pedro; Direito, BrunoMusic can convey fundamental emotions like happiness and sadness and more intricate feelings such as tenderness or grief. Understanding the neural mechanisms underlying music-induced emotions holds promise for innovative, personalised neurorehabilitation therapies using music. Our study investigates the link between perceived emotions in music and their corresponding neural responses, measured using fMRI. Fifteen participants underwent fMRI scans while listening to 96 musical excerpts categorised into quadrants based on Russell’s valence-arousal model. Neural correlates of valence and arousal were identified in neocortical regions, especially within music-specific sub-regions of the auditory cortex. Through multivariate pattern analysis, distinct emotional quadrants were decoded with an average accuracy of 62% ±15%, surpassing the chance level of 25%. This capacity to discern music’s emotional qualities has implications for psychological interventions and mood modulation, enhancing music-based treatments and neurofeedback learning.
- A Comparison Study of Deep Learning Methodologies for Music Emotion RecognitionPublication . 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 necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning.
- Improving Deep Learning Methodologies for Music Emotion RecognitionPublication . 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, high-quality labeled datasets, a well-known hurdle in MER studies. We present a comparative study of various classical and DL techniques carried out to evaluate these approaches. Most of the presented methodologies were developed by our team, if not stated otherwise. It was found that a combination of Dense Neural Networks (DNN) and Convolutional Neural Networks (CNN) achieved an 80.20% F1-score, marking an improvement of approximately 5% over the best previous results. This indicates that future research should blend both manual feature engineering and automated feature learning to enhance results.
- VITASENIOR–MT: Architecture of a Telehealth SolutionPublication . Pires, Gabriel; Lopes, Ana; Manso, António; Jorge, Dário; Mendes, Diogo; Almeida, Luis; Lopes de Oliveira, Luís Miguel; Gomes, Nelson; Dias, Pedro; Panda, Renato; Pereira, Telmo; Monteiro, Paulo; Grácio, CarlaVITASENIOR-MT is a telehealth solution under development that aims to monitor and improve the healthcare of elderly people living in the region of Médio Tejo. This solution performs both remote and local monitoring of biometric parameters of the elderly, and also of environmental parameters of their homes. The biometric variables include heart rate and temperature measurements collected automatically, by means of a bracelet, throughout the day. Blood pressure, body weight, and other biometric parameters are measured on a daily basis by the senior’s own initiative, and automatically recorded. The environmental parameters include temperature, carbon monoxide and carbon dioxide measurements. A TV set is used as a mean of interaction between the user and the medical devices. The TV set is also used to receive medical warnings and recommendations according to clinical profiles, and to receive environmental alerts. All data and alerts can be accessible to senior’s family and healthcare providers. In alarm situations, an automatic operational procedure will be triggered establishing communication to predefined entities.
- Inteligência para a Sustentabilidade das CidadesPublication . Pinho, Henrique J. O.; Lopes de Oliveira, Luís Miguel; Coelho, Paulo; Frazão Correia, Pedro; Panda, Renato
- Audio Features for Music Emotion Recognition: a SurveyPublication . Panda, Renato; Malheiro, Ricardo; Paiva, Rui PedroThe design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed.
- O papel da inovação tecnológica e da ciência aberta no desenvolvimento sustentável das cidades e regiões – Um caso práticoPublication . Barros, F.M. Manuel; Pinho, Henrique J. O.; Frazão Correia, Pedro; Panda, Renato; Silva, Gonçalo