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

Seismology-based early identification of dam-formation landquake events

Flooding resulting from the bursting of dams formed by landquake events such as rock avalanches, landslides and debris flows can lead to serious bank erosion and inundation of populated areas near rivers. Seismic waves can be generated by landquake events which can be described as time-dependent forces (unloading/reloading cycles) acting on the Earth. In this study, we conduct inversions of long-period (LP, period ≥20 s) waveforms for the landquake force histories (LFHs) of ten events, which provide quantitative characterization of the initiation, propagation and termination stages of the slope failures. When the results obtained from LP waveforms are analyzed together with high-frequency (HF, 1–3 Hz) seismic signals, we find a relatively strong late-arriving seismic phase (dubbed Dam-forming phase or D-phase) recorded clearly in the HF waveforms at the closest stations, which potentially marks the time when the collapsed masses sliding into river and perhaps even impacting the topographic barrier on the opposite bank. Consequently, our approach to analyzing the LP and HF waveforms developed in this study has a high potential for identifying five dam-forming landquake events (DFLEs) in near real-time using broadband seismic records, which can provide timely warnings of the impending floods to downstream residents

Seismologically determined bedload flux during the typhoon season

Continuous seismic records near river channels can be used to quantify the energy induced by river sediment transport. During the 2011 typhoon season, we deployed a seismic array along the Chishan River in the mountain area of southern Taiwan, where there is strong variability in water discharge and high sedimentation rates. We observe hysteresis in the high-frequency (5–15 Hz) seismic noise level relative to the associated hydrological parameters. In addition, our seismic noise analysis reveals an asymmetry and a high coherence in noise cross-correlation functions for several station pairs during the typhoon passage, which corresponds to sediment particles and turbulent flows impacting along the riverbed where the river bends sharply. Based on spectral characteristics of the seismic records, we also detected 20 landslide/debris flow events, which we use to estimate the sediment supply. Comparison of sediment flux between seismologically determined bedload and derived suspended load indicates temporal changes in the sediment flux ratio, which imply a complex transition process from the bedload regime to the suspension regime between typhoon passage and off-typhoon periods. Our study demonstrates the possibility of seismologically monitoring river bedload transport, thus providing valuable additional information for studying fluvial bedrock erosion and mountain landscape evolution

implications for health and disease

Many aspects of human physiology and behavior display rhythmicity with a period of approximately 24 h. Rhythmic changes are controlled by an endogenous time keeper, the circadian clock, and include sleep-wake cycles, physical and mental performance capability, blood pressure, and body temperature. Consequently, many diseases, such as metabolic, sleep, autoimmune and mental disorders and cancer, are connected to the circadian rhythm. The development of therapies that take circadian biology into account is thus a promising strategy to improve treatments of diverse disorders, ranging from allergic syndromes to cancer. Circadian alteration of body functions and behavior are, at the molecular level, controlled and mediated by widespread changes in gene expression that happen in anticipation of predictably changing requirements during the day. At the core of the molecular clockwork is a well-studied transcription-translation negative feedback loop. However, evidence is emerging that additional post-transcriptional, RNA-based mechanisms are required to maintain proper clock function. Here, we will discuss recent work implicating regulated mRNA stability, translation and alternative splicing in the control of the mammalian circadian clock, and its role in health and disease

Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance

Score-based Generative Models (SGMs) are a popular family of deep generative models that achieves leading image generation quality. Earlier studies have extended SGMs to tackle class-conditional generation by coupling an unconditional SGM with the guidance of a trained classifier. Nevertheless, such classifier-guided SGMs do not always achieve accurate conditional generation, especially when trained with fewer labeled data. We argue that the issue is rooted in unreliable gradients of the classifier and the inability to fully utilize unlabeled data during training. We then propose to improve classifier-guided SGMs by letting the classifier calibrate itself. Our key idea is to use principles from energy-based models to convert the classifier as another view of the unconditional SGM. Then, existing loss for the unconditional SGM can be adopted to calibrate the classifier using both labeled and unlabeled data. Empirical results validate that the proposed approach significantly improves the conditional generation quality across different percentages of labeled data. The improved performance makes the proposed approach consistently superior to other conditional SGMs when using fewer labeled data. The results confirm the potential of the proposed approach for generative modeling with limited labeled data