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

Rupture of Renal Pelvis in an Adult with Congenital Ureteropelvic Junction Obstruction After Blunt Abdominal Trauma

Isolated injury to the renal pelvis following blunt abdominal trauma is very rare. However, a pre-existing renal abnormality will increase the risk of rupture. We present a 24-year-old man with rupture of the left renal pelvis following blunt abdominal trauma. He had pre-existing left ureteropelvic junction (UPJ) obstruction. Delayed computed tomography scan with excretory phase revealed contrast medium extravasation from the left UPJ, and left renal pelvis rupture was diagnosed. He was managed successfully with ureteral double-J stenting for 2 months

Effect on Spasticity After Performance of Dynamic-Repeated-Passive Ankle Joint Motion Exercise in Chronic Stroke Patients

Spasticity associated with abnormal muscle tone is a common motor disorder following stroke, and the spastic ankle may affect ambulatory function. The purpose of this study was to investigate the short-term effect of dynamic-repeated-passive ankle movements with weight loading on ambulatory function and spastic hypertonia of chronic stroke patients. In this study, 12 chronic stroke patients with ankle spasticity and inefficient ambulatory ability were enrolled. Stretching of the plantar-flexors of the ankle in the standing position for 15 minutes was performed passively by a constant-speed and electrically powered device. The following evaluations were done before and immediately after the dynamic-repeated-passive ankle movements. Spastic hypertonia was assessed by the Modified Ashworth Scale (MAS; range, 0–4), Achilles tendon reflexes test (DTR; range, 0–4), and ankle clonus (range, 0–5). Improvement in ambulatory ability was determined by the timed up-and-go test (TUG), the 10-minute walking test, and cadence (steps/minute). In addition, subjective experience of the influence of ankle spasticity on ambulation was scored by visual analog scale (VAS). Subjective satisfaction with the therapeutic effect of spasticity reduction was evaluated by a five-point questionnaire (1 = very poor, 2 = poor, 3 = acceptable, 4 = good, 5 = very good). By comparison of the results before and after intervention, these 12 chronic stroke patients presented significant reduction in MAS and VAS for ankle spasticity, the time for TUG and 10-minute walking speed (p < 0.01). The cadence also increased significantly (p < 0.05). In addition, subjective satisfaction with the short-term therapeutic effect was mainly good (ranging from acceptable to very good). In conclusion, 15 minutes of dynamic-repeated-passive ankle joint motion exercise with weight loading in the standing position by this simple constant-speed machine is effective in reducing ankle spasticity and improving ambulatory ability

Credit Scoring Based on Hybrid Data Mining Classification

The credit scoring has been regarded as a critical topic. This study proposed four approaches combining with the NN (Neural Network) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different approaches combined with NN classifier were constructed by selecting features. NN classifier combines with conventional statistical LDA, Decision tree, Rough set and F-score approaches as features preprocessing step to optimize feature space by removing both irrelevant and redundant features. The procedure of the proposed algorithm is described first and then evaluated by their performances. The results are compared in combination with NN classifier and nonparametric Wilcoxon signed rank test will be held to show if there has any significant difference between these approaches. Our results suggest that hybrid credit scoring models are robust and effective in finding optimal subsets and the compound procedure is a promising method to the fields of data mining

CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment

Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing. Traditional intrusive speech quality assessment requires the clean reference of the degraded utterance to provide an accurate quality measurement. This requirement limits the usability of these methods in real-world scenarios. On the other hand, non-intrusive subjective measurement is the ``golden standard" in evaluating speech quality as human listeners can intrinsically evaluate the quality of any degraded speech with ease. In this paper, we propose a novel end-to-end model structure called Convolutional Context-Aware Transformer (CCAT) network to predict the mean opinion score (MOS) of human raters. We evaluate our model on three MOS-annotated datasets spanning multiple languages and distortion types and submit our results to the ConferencingSpeech 2022 Challenge. Our experiments show that CCAT provides promising MOS predictions compared to current state-of-art non-intrusive speech assessment models with average Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697 and average RMSE decreasing from 0.768 to 0.570 compared to the baseline model on the challenge evaluation test set

Comparison of secondary signs as shown by unenhanced helical computed tomography in patients with uric acid or calcium ureteral stones

AbstractUnenhanced helical computed tomography (UHCT) has evolved into a well-accepted diagnostic method in patients with suspected ureterolithiasis. UHCT not only shows stones within the lumen of the ureter, it also permits evaluation of the secondary signs associated with ureteral obstruction from stones. However, there we could find no data on how secondary signs might differ in relation to different compositions of ureteral stones. In this study, we compared the degree of secondary signs revealed by UHCT in uric acid stone formers and in patients forming calcium stones. We enrolled 117 patients with ureteral stones who underwent UHCT examination and Fourier transform infra-red analysis of stone samples. Clinical data were collected as follows: age, sex, estimated glomerular filtration rate (eGFR), urine pH, and radiological data on secondary signs apparent on UHCT. The uric acid stone formers had significantly lower urine pH and eGFR in comparison to calcium stone formers, and on UHCT they also had a higher percentage of the secondary signs, including rim sign (78.9% vs. 60.2%), hydroureter (94.7% vs. 89.8%), perirenal stranding (84.2% vs. 59.2%) and kidney density difference (73.7% vs. 50.0%). The radiological difference was statistically significant for perirenal stranding (p=0.041). In conclusion, we found that UHCT scanning reveals secondary signs to be more frequent in patients with uric acid ureteral stones than in patients with calcium stones, a tendency that might result from an acidic urine environment

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD

Comprehensive Numerical Assessment of Rotorcraft Vibration and Noise Control Using Microflaps

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140642/1/1.c033448.pd

Illiquidity Premium and Monetary Conditions in Emerging Markets: An Empirical Examination of Taiwan Stock Markets

This study empirically examines the illiquidity premium of Taiwan stock markets and its relationship with monetary policies. We find that commonly used illiquidity measures are generally sensitive and capable of capturing market illiquidity, particularly during the most volatile periods. Evidence shows that unconditional illiquidity is significantly priced across three illiquidity measures during the sample period. Aggregate market illiquidity innovations are noticeably affected by monetary policies. The results of Granger causality tests reveal that expansive monetary policy improves market illiquidity, whereas restrictive policy adversely affects market liquidity. Keywords: Illiquidity; illiquidity premium; monetary policy; asset pricing; Granger's causality tests JEL Classifications: G11, G12, G15 DOI: https://doi.org/10.32479/ijefi.895