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This paper presents an argument for the necessity of a large vocabulary in automatic chord recognition systems, on the grounds of the requirements of machine musicianship. It proposes a system fram...

Music listening is an integral part of many adolescents? everyday lives, but it is also a time when adolescents are uniquely vulnerable. Emotional-oriented and avoidance through listening to unsuitable music may bring negative emotion to adolescents and increase the level of their depression. We propose MeDJ, a multidimensional emotion-aware music delivery application, which turns adolescents' music listening into a health and pleasure way. MeDJ aims at helping adolescents to improve emotional management and prevent depression. It is built on a cloud-based platform that enables adolescents and their peers collaboratively recommend suitable music to each other through smartphones. Prototype implementation and initial results of MeDJ have demonstrated its practicability for real-world deployment.

This paper proposes an automatic chord estimation (ACE) system with a two-layer architecture. The first layer performs chord smoothing with "GMM + HMM" approach. Then given the results of the first layer, the second layer performs chord estimation using a deep neural network, which is trained on a well chord-type balanced dataset. The system accepts exactly the "SeventhsBass" vocabulary. Three approaches with different configurations of the system are compared with Chordino, which is probably the only both MIREX evaluated and "SeventhsBass" acceptable ACE system. Evaluation results on "The Beatles" dataset show that the best approach outperforms Chordino in the most difficult "SeventhsBass" metric in a significant way.

Road safety is a huge concern due to the large number of fatalities and injuries caused by road accidents. Research has shown that fatigue can adversely affect driving performance and increase risk of road accidents. It has been shown that driving performance is enhanced by stress-relieving music which thereby promotes safer driving. Context-aware music delivery systems promote safer driving through intelligent music recommendations based on contextual knowledge. Two key aspects of situation-aware music delivery are effectiveness and efficiency of music recommendation. Efficiency is a critical aspect in real-time context based music recommendation as the music delivery system should quickly sense any change in the situation and deliver suitable music before the sensed context-data becomes obsolete. We focus on the efficiency of situation-aware music delivery systems in this paper. Music mood-mapping is a process which helps in understanding the mood of a song and is hence used in situation-aware music recommendation systems. This process requires a large processing time due to the complex calculations and large sizes of music files involved. Hence, optimizing this process is the key to improving the efficiency of context-aware music delivery systems. Here, we propose a novel cloud and crowd-sensing based approach to considerably optimize the efficiency of situation-aware music delivery systems.

Millions of people are severely injured or killed in road accidents every year and most of these accidents are caused by human error. Fatigue and negative emotions such as anger adversely affect driver performance, thereby increasing the risk involved in driving. Research has shown that listening to the right kind of music in these situations can ameliorate driver performance and improve road safety. Context-aware music delivery systems succeed in delivering suitable music according to the situation through the process of music mood-mapping which identifies the mood of a song. Additionally, we can leverage the power of the cloud to enable crowd sensing of the mood-mapping of various songs and enhance the effectiveness of situation-aware music delivery for drivers. The cloud can be used to aggregate the crowd sensed music mood-mapping data and improve the effectiveness of music delivery by providing accurate mood-mappings from the aggregated data. Currently, context-aware music delivery systems consider only features from the song for music mood-mapping. In this paper, we propose a novel approach to music mood-mapping for drivers which also incorporates the social context of a driver including age, gender and cultural background to enhance the effectiveness of music delivery in context-aware music recommendation systems for drivers.

A wide-range and phase-locked Michelson interferometer technique is described. This technique combined with femtosecond laser is used to measure the spectrum of the rare-earth ion Nd:YVO4, which presents very high signal to noise ratio of interferometric intensity output and higher spectral resolution than traditional grating spectrophotometer.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Previous research has demonstrated that not only does listening to suitable music while driving not impair driving performance, but it could lead to an improved mood and a more relaxed body state, which could improve driving performance and promote safe driving significantly. In this article, we propose SAfeDJ, a smartphone-based situation-aware music recommendation system, which is designed to turn driving into a safe and enjoyable experience. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. Its design is based on novel interactive methods, which enable in-car smartphones to orchestrate multiple sources of sensing data and the drivers' social context, in collaboration with cloud computing to form a seamless crowdsensing solution. This solution enables different smartphones to collaboratively recommend preferable music to drivers according to each driver's specific situations in an automated and intelligent manner. Practical experiments of SAfeDJ have proved its effectiveness in music-mood analysis, and mood-fatigue detections of drivers with reasonable computation and communication overheads on smartphones. Also, our user studies have demonstrated that SAfeDJ helps to decrease fatigue degree and negative mood degree of drivers by 49.09% and 36.35%, respectively, compared to traditional smartphone-based music player under similar driving situations.

Nowadays more and more organizations focus on reducing traffic accidents and defensive measures for safe driving. The vigilance level (e.g., negative emotion and fatigue) also accounts for the road injuries. Till now, there is no systematic solution for different mobile devices that can effectively infer the mood and fatigue of drivers in real-time or conveniently be used by drivers, nor incentive scheme for drivers in large scale to stimulate their positive and secure driving collaboratively with friends in a social context. In this paper, we propose the Mood-Fatigue Analyzer (MFA), a systematic solution that can be used in different middlewares on mobile devices, which can transform the data from sensors to context-aware mobile sensing applications for safe driving. The MFA employs multidimensional methods to get the drivers' real-time mood and fatigue information by sensors using the Internet of Things (IoT) deployed in and out of cars. Besides promoting safe driving with integrated sensors, the MFA could be built on a multi-tier vehicular social network (VSN) platform, which enables communication among drivers in a social context via cloud platform. Architecture implementation and experimental results of the MFA have demonstrated its desired functionalities and efficiency in drivers' daily lives and real-world deployment.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Poor road condition, traffic congestion and long driving time may bring negative emotion to drivers and increase the chance of traffic accidents. We propose SAfeDJ, a situation-aware in-car music delivery application, which turns people's trips into pleasant journeys and driving into a safe and enjoyable activity. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. It is built on a vehicular healthcare platform that enables communications among drivers and integrates with multiple types of sensors to promote safe driving. Prototype implementation and initial results of SAfeDJ have demonstrated its desired functionality in drivers' daily lives and feasibility for real-world deployment.

An alternating magnetic field (AMF) apparatus is developed and composed of an electromagnet and driving power supply. The structure of the electromagnet and configuration of the driving supply are described in detail. The apparatus can produce a peak magnetic field up to 9000 Oe and above under a small driving power at its resonance frequency of 1.14 kHz. Based on synchronization between the AMF and the femtosecond laser pulse train, a photomagnetic synchronized time- and high-field-resolved all-optical pump-probe magnetic-optical setup is developed. This setup has the ability to reinitialize any magnetic states between two successive laser pulses so that irreversible magnetization reversal dynamics can be studied. Dynamic Kerr hysteresis loops and magnetization reversal dynamics of high coercivity ferromagnetic TbFeCo and FePt films are demonstrated using this setup, showing importance of this synchronized time- and high-field-resolved all-optical pump-probe magnetic-optical setup in the research of ultrafast magnetization dynamics.

A high signal-to-noise-ratio (SNR) and resolution Fourier spectrometer based on a wide-range and phase-locked Michelson interferometer is presented. A novel arrangement of a step motor, a nanometer resolution piezoelectric actuator and an active PID control loop ensures that the Michelson interferometer has a phase stabilized scanning range virtually without limit, resulting in the Fourier spectrometer's high resolution and high SNR. Several experiments demonstrate that this spectrometer, using light sources such as write LED (WLED) and femtosecond laser, can accurately acquire the spectrum information of a given sample as well as the light source itself in a resolution of nanometer order.