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

COMPARISON OF MOVEMENT CHARACTERISTIC AND MUSCLE ACTIVATION BETWEEN DIFFERENT FITNESS HOOPS

Purpose: To compare the movement characteristics and muscle activation between Hula Hoop (HL) and Mini Hoop (MH). Methods: Sixteen healthy females randomly used HL and MH three minutes, respectively. Motion Analysis System and Noraxon wireless surface electromyography (EMG) were used to measure the movement characteristics and muscle activation. The paired t-test was used to test the difference between MH and HL. Results: The HL had larger in range of hip motion and root mean square of EMG in spinal erectors than MH (p < .05); the MH had higher in movement frequency (cycles per second) and median frequency of EMG in spinal erectors than HL (p < .05). Conclusion: Two fitness hoops have different movement characteristics and muscle action due to the different equipment design