6,119 research outputs found
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP
How does Organic Agriculture Contribute to Sustainable Development? Organic Agriculture in Taiwan
Sustainability issues in agrifood chains are receiving increasing attention. However, few studies have demonstrated the dynamic interrelationships between economic, environmental, and social indicators. Regarding these indicators as components of sustainable development, through sensitivity simulations, we found that (1) organic farming techniques as key to environmental and economic improvement by indirect sales and (2) direct sales channels can strengthen environmental and social benefits. The findings suggest that developing diversified production and sales channels is essential for the sustainable development of organic agriculture to maintain economic, social, and environmental sustainability
The Evolution of Internal Representation
To develop an appropriate internal representation, a deterministic learning algorithm that has an ability to adjust not only weights but also the number of adopted hidden nodes is proposed. The key mechanisms are (1) the recruiting mechanism that recruits proper extra hidden nodes, and (2) the reasoning mechanism that prunes potentially irrelevant hidden nodes. This learning algorithm can make use of external environmental clues to develop an internal representation appropriate for the required mapping. The encoding problem and the parity problem is used to demonstrate the performance of the proposed algorithm. The experimental results are clearly positive
PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation
Fall accidents are critical issues in an aging and aged society. Recently,
many researchers developed pre-impact fall detection systems using deep
learning to support wearable-based fall protection systems for preventing
severe injuries. However, most works only employed simple neural network models
instead of complex models considering the usability in resource-constrained
mobile devices and strict latency requirements. In this work, we propose a
novel pre-impact fall detection via CNN-ViT knowledge distillation, namely
PreFallKD, to strike a balance between detection performance and computational
complexity. The proposed PreFallKD transfers the detection knowledge from the
pre-trained teacher model (vision transformer) to the student model
(lightweight convolutional neural networks). Additionally, we apply data
augmentation techniques to tackle issues of data imbalance. We conduct the
experiment on the KFall public dataset and compare PreFallKD with other
state-of-the-art models. The experiment results show that PreFallKD could boost
the student model during the testing phase and achieves reliable F1-score
(92.66%) and lead time (551.3 ms)
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