MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at https://salu133445.github.io/musegan/ .Comment: to appear at AAAI 201

An Intelligent Auxiliary Vacuum Brake System

The purpose of this paper focuses on designing an intelligent, compact, reliable, and robust auxiliary vacuum brake system (VBS) with Kalman filter and self-diagnosis scheme. All of the circuit elements in the designed system are integrated into one programmable system-on-chip (PSoC) with entire computational algorithms implemented by software. In this system, three main goals are achieved: (a) Kalman filter and hysteresis controller algorithms are employed within PSoC chip by software to surpass the noises and disturbances from hostile surrounding in a vehicle. (b) Self-diagnosis scheme is employed to identify any breakdown element of the auxiliary vacuum brake system. (c) Power MOSFET is utilized to implement PWM pump control and compared with relay control. More accurate vacuum pressure control has been accomplished as well as power energy saving. In the end, a prototype has been built and tested to confirm all of the performances claimed above

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

The Effects Of Persuasive Messages On System Acceptance

Firms have to invest millions of dollars to introduce a new system. If firms cannot persuade employees to accept and implement a system effectively, such investments are wasted. Since a given influence process may lead to differential outcomes, managers need to deliver influencing strategies to motivate employees and shape their behavior intentions related to system acceptance. This study integrates TAM, flow theory, and extends ELM to understand employees’ system acceptance. The findings indicate that two persuasive messages result in different influencing routes on employees’ emotional, functional, and utilitarian responses. Source credibility of persuasive messages has positive influence on playfulness, while argument quality of persuasive messages has positive influence on perceived ease of use and perceived usefulness. Attitude may play mediating roles in the relationship of playfulness-behavior intention and perceived usefulness-behavior intention

Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging technology which provides cloud service in the edge of multi-radio access networks aims to reduce the service latency experienced by end devices. When individual MEC systems do not have adequate resource capacity to fulfill service requests, forming MEC federations for resource sharing could provide economic incentive to MEC operators. To this end, we need to maximize social welfare in each federation, which involves efficient federation structure generations, federation profit maximization by resource provisioning configuration, and fair profit distribution among participants. We model the problem as a coalition game with difference from prior work in the assumption of latency and locality constraints and also in the consideration of various service policies/demand preferences. Simulation results show that the proposed approach always increases profits. If local requests are served with local resource with priority, federation improves profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586)

Reward prediction errors arising from switches between major and minor modes in music: An fMRI study

Evidence has accumulated that prediction error processing plays a role in the enjoyment of music listening. The present study examined listeners' neural responses to the signed reward prediction errors (RPEs) arising from switches between major and minor modes in music. We manipulated the final chord of J. S. Bach's keyboard pieces so that each major-mode passage ended with either the major (Major-Major) or minor (Major-Minor) tonic chord, and each minor-mode passage ended with either the minor (Minor-Minor) or major (Minor-Major) tonic chord. In Western music, the major and minor modes have positive and negative connotations, respectively. Therefore, the outcome of the final chord in Major-Minor stimuli was associated with negative RPE, whereas that in Minor-Major was associated with positive RPE. Twenty-three musically experienced adults underwent functional magnetic resonance imaging while listening to Major-Major, Major-Minor, Minor-Minor, and Minor-Major stimuli. We found that activity in the subgenual anterior cingulate cortex (extending into the ventromedial prefrontal cortex) during the final chord for Major-Major was significantly higher than that for Major-Minor. Conversely, a frontoparietal network for Major-Minor exhibited significantly increased activity compared to Major-Major. The contrasts between Minor-Minor and Minor-Major yielded regions implicated in interoception. We discuss our results in relation to executive functions and the emotional connotations of major versus minor mode.Comment: submitted to Psychophysiolog

Distributed multi-user MIMO transmission using real-time sigma-delta-over-fiber for next generation fronthaul interface

To achieve the massive device connectivity and high data rate demanded by 5G, wireless transmission with wider signal bandwidths and higher-order multiple-input multiple-output (MIMO) is inevitable. This work demonstrates a possible function split option for the next generation fronthaul interface (NGFI). The proof-of-concept downlink architecture consists of real-time sigma-delta modulated signal over fiber (SDoF) links in combination with distributed multi-user (MU) MIMO transmission. The setup is fully implemented using off-the-shelf and in-house developed components. A single SDoF link achieves an error vector magnitude (EVM) of 3.14% for a 163.84 MHz-bandwidth 256-QAM OFDM signal (958.64 Mbps) with a carrier frequency around 3.5 GHz transmitted over 100 m OM4 multi-mode fiber at 850 nm using a commercial QSFP module. The centralized architecture of the proposed setup introduces no frequency asynchronism among remote radio units. For most cases, the 2 x 2 MU-MIMO transmission has little performance degradation compared to SISO, 0.8 dB EVM degradation for 40.96 MHz-bandwidth signals and 1.4 dB for 163.84 MHz-bandwidth on average, implying that the wireless spectral efficiency almost doubles by exploiting spatial multiplexing. A 1.4 Gbps data rate (720 Mbps per user, 163.84 MHz-bandwidth, 64-QAM) is reached with an average EVM of 6.66%. The performance shows that this approach is feasible for the high-capacity hot-spot scenario