This paper presents a novel pedestrian detection method based on the use of a convolutional neural network (CNN) classifier. Our method achieves high accuracy by automatidly optimizing the feature representation to the detection task and regularizing the neural network.We evaluate the proposed method on a diffcult database containing pedestrians in a city environment with no restrictions on pose, action, background and lighting conditions. The false positive rate (FPR) of the proposed CNN classifier is less than 1/5-th of the FPR of a support vector machine (SVM) classifier using Hilar-wavelet features when the detection rate is 90%. The accuracy of the SVM classifier s i n g the features learnt by the CNN is equivalent lo the accuracy of the CNN,

Abstract-This paper describes a system that can automatically synchronize polyphonic musical audio signals with their corresponding lyrics. Although methods for synchronizing monophonic speech signals and corresponding text transcriptions by using Viterbi alignment techniques have been proposed, these methods cannot be applied to vocals in CD recordings because vocals are often overlapped by accompaniment sounds. In addition to a conventional method for reducing the influence of the accompaniment sounds, we therefore developed four methods to overcome this problem: a method for detecting vocal sections, a method for constructing robust phoneme networks, a method for detecting fricative sounds, and a method for adapting a speech-recognizer phone model to segregated vocal signals. We then report experimental results for each of these methods and also describe our music playback interface that utilizes our system for synchronizing music and lyrics.

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This paper describes a system that can automatically synchronize between polyphonic musical audio signals and corresponding lyrics. Although there were methods that can synchronize between monophonic speech signals and corresponding text transcriptions by using Viterbi alignment techniques, they cannot be applied to vocals in CD recordings because accompaniment sounds often overlap with vocals. To align lyrics with such vocals, we therefore developed three methods: a method for segregating vocals from polyphonic sound mixtures, a method for detecting vocal sections, and a method for adapting a speech-recognizer phone model to segregated vocal signals. Experimental results for 10 Japanese popular-music songs showed that our system can synchronize between music and lyrics with satisfactory accuracy for 8 songs.

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