Subjective Similarity of Music: Data Collection for Individuality Analysis

Abstract-We describe a method of estimating subjective music similarity from acoustic music similarity. Recently, there have been many studies on the topic of music information retrieval, but there continues to be difficulty improving retrieval precision. For this reason, in this study we analyze the individuality of subjective music similarity. We collected subjective music similarity evaluation data for individuality analysis using songs in the RWC music database, a widely used database in the field of music information processing. A total of 27 subjects listened to pairs of music tracks, and evaluated each pair as similar or dissimilar. They also selected the components of the music (melody, tempo/rhythm, vocals, instruments) that were similar. Each subject evaluated the same 200 pairs of songs, thus the individuality of the evaluation can be easily analyzed. Using the collected data, we trained individualized distance functions between songs, in order to estimate subjective similarity and analyze individuality

Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer

Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipeline, limiting their availability and robustness. To address these issues, we introduce a novel deep learning based action recognition framework for classifying dangerous lane change behavior in short video clips captured by a monocular camera. We designed a deep spatiotemporal classification network that uses pre-trained state-of-the-art instance segmentation network Mask R-CNN as its spatial feature extractor for this task. The Long-Short Term Memory (LSTM) and shallower final classification layers of the proposed method were trained on a semi-naturalistic lane change dataset with annotated risk labels. A comprehensive comparison of state-of-the-art feature extractors was carried out to find the best network layout and training strategy. The best result, with a 0.937 AUC score, was obtained with the proposed network. Our code and trained models are available open-source.Comment: 8 pages, 3 figures, 1 table. The code is open-sourc

Driving Signature Extraction

This study proposes a method to extract the unique driving signatures of individual drivers. We assume that each driver has a unique driving signature that can be represented in a k dimensional principal driving component (PDC) space. We propose a method to extract this signature from sensor data. Furthermore, we suggest that drivers with similar driving signatures can be categorized into driving style classes such as aggressive or careful driving. In our experiments, 122 different drivers have driven the same path on Nagoya city express highway with the same instrumented car. GPS, speed, acceleration, steering wheel position and pedal operations have been recorded. Clustering methods have been used to identify driving signatures

Driving Signature Extraction

This study proposes a method to extract the unique driving signatures of individual drivers. We assume that each driver has a unique driving signature that can be represented in a k dimensional principal driving component (PDC) space. We propose a method to extract this signature from sensor data. Furthermore, we suggest that drivers with similar driving signatures can be categorized into driving style classes such as aggressive or careful driving. In our experiments, 122 different drivers have driven the same path on Nagoya city express highway with the same instrumented car. GPS, speed, acceleration, steering wheel position and pedal operations have been recorded. Clustering methods have been used to identify driving signatures