Jun-qi Deng

A Novel Cloud-Based Crowd Sensing Approach to Context-Aware Music Mood-Mapping for Drivers

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.

Large vocabulary automatic chord estimation using bidirectional long short-term memory recurrent neural network with even chance training

Abstract 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 framework with a skewed class-sensitive training scheme that leads to a preliminary solution to large vocabulary automatic chord estimation. This framework applies a bidirectional long short-term memory recurrent neural network architecture, which employs an ‘even chance’ training scheme to make up for the lack of uncommon chords’ exposure. The main drawback of this approach is the low segmentation quality, which inevitably lowers the upper bound of chord estimation accuracy. Under a large vocabulary evaluation, the proposed system can significantly outperform the baseline system in terms of the overall weighted chord symbol recall, and there is no significant difference between them in terms of average chord quality accuracy. The results demonstrate preliminary success in our approach, and also prove the even chance training scheme to be effective in boosting uncommon chord symbol recalls as well as the average chord quality accuracy.

Towards In Time Music Mood-Mapping for Drivers: A Novel Approach

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.