IPT - Ci2 - Artigos em Conferências
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- "Back in my day...": A Preliminary Study on the Differences in Generational Groups Perception of Musically-evoked EmotionPublication . Louro, 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 perception of music between the Millennials and Gen Z generations. Interviews were conducted with six participants equally distributed between both generations by recording their listening experience and emotion perception on two previously compiled sets of songs representing each group. Significant differences between generations and possible contributing factors were found in the analysis of the conducted interviews. Findings point to differences in the perception of energy of songs with specific messages of suffering for love, as well as a tendency from the younger group to perceive a well-defined emotion in songs representing their generation in contrast to neutral responses from the other group. These findings are preliminary, and further studies are needed to understand their extent. Nevertheless, valuable insights can be extracted to improve music recommendation systems.
- Contribution of Constructed Wetlands for Reclaimed Water Production: A ReviewPublication . Pinho, Henrique J. O.; Mateus, D. M. R.Freshwater scarcity is a growing threat to sustainable development, which can be mitigated by adequate management of water resources. Agriculture and related activities consist in the main use of freshwater, but several other human activities present relevant contributions. Because most of the water uses imply the generation of resultant wastewater, the production and use of reclaimed water by appropriate technologies can be part of the solution to that issue. Considering that the use of constructed wetlands (CWs) can be a relevant contribution to the production of reclaimed water, as an eco-friendly alternative to costly advanced water treatment technologies, this work is a review of the last decade of literature on the use of CWs to produce reclaimed water. The results point to a usual focus on the production of reclaimed water for agriculture or urban spaces irrigation. In order to potentiate a broader application of CWs, some directions of future research and use of this green technology are proposed.
- Cultivation of Energy Crops in Constructed Wetlands for Wastewater Treatment: An OverviewPublication . Pinho, Henrique J. O.; Mateus, D. M. R.The need for sustainable, clean, and secure energy sources is a current issue for all nations. All kinds of vegetal biomass can be used as energy-source or as raw material for biofuel production, but some species are commonly classified as energy crops. This work evaluates the energy potential of 35 species of energy crops when produced in constructed wetlands (CW). Producing energy crops in CW is a route to link wastewater treatment to energy production, avoiding the abstraction of freshwater for crop irrigation, and simultaneously avoiding the use of arable land. However, for most of the energy crops, there are no data available in the literature about biomass productivity in CWs. Although 20 of the 35 crops have been tested as CW vegetation, the biomass productivity in CWs was only found for 13 species. Reported biomass productivity in CW is similar to or even higher than the productivity reported for conventional production, but most reported data is for pilot-scale CW, which points to the need for future work in full-scale systems. From the combination of biomass productivity and the biomass calorific value, Arundo donax, Miscanthus x giganteus, Cynodon dactylon, Phragmites australis, and Typha latifolia show higher ranges up to 3064 MJ/ha year for Arundo donax. Future works on CW design can be focused on the potential of using energy crops as vegetation.
- Energy Mix in the Production of HydrogenPublication . Pereira, Carlos; Coelho, Paulo; Fernandes, José; Gomes, MárioThis paper presents a study related to the production of electricity through a mini-hydro plant (MHP) and a photovoltaic (PV) system particularly sized for a location in Tomar (Portugal). A system based on this energy mix is adopted in order to produce hydrogen (H2) and oxygen (O2) at high pressure for energy storage purposes. The main features of the different equipments chosen in this study are also presented in the paper.
- Exploring 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 (MER). 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, conbining handcrafted features with feature learning.
- Exploring Song Segmentation for Music Emotion Variation DetectionPublication . 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 promise, the potential of this song segmentation approach was not fully exploited, possibly due to the small employed dataset. Nevertheless, preliminary results are encouraging.
- Head-movement interface for wheelchair driving based on inertial sensorsPublication . Gomes, Daniel; Fernandes, Filipe; Castro, Eduardo; Pires, GabrielPowered wheelchairs provide the only means of mobility for many people with severe motor disabilities. For those with both lower and upper limbs impairment, available interfaces may be either impossible or very difficult to use, as well as not very efficient. In this paper we propose an egocentric interface based on inertial sensors placed on the user's head. This interface is based on head movements that provide continuous direction and speed commands to steer the wheelchair, and allows an initial null-position of the head according to the natural posture of the user. However, the development of an inertial interface for driving a wheelchair presents two main challenges, namely, (1) the simultaneous movements of the head and the wheelchair, each one with its own coordinate system, and (2) the free unrestricted movement of the head. Therefore, the two coordinate systems need to be combined and several safety features are required to only ensure admissible commands. In this paper we describe the overall implementation and preliminary experiments that show the effectiveness of the proposed solution.
- 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.
- Hydrogen Production via Wastewater Electrolysis – An Integrated Approach ReviewPublication . Cartaxo, Marco; Fernandes, José; Gomes, Mário; Pinho, Henrique J. O.; Nunes, Valentim; Coelho, PauloHuman activities generate enormous amounts of wastewater. The hydrogen production from this new resource has gained attention as an emergent technology. Incorporating photovoltaic energy production with different electrolysis systems which can treat wastewaters and produce hydrogen simultaneously will lead to an environmentally-friendly and sustainable hydrogen production.
- 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.
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