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.

Abstract-Aligning lyrics to audio has a wide range of applications such as the automatic generation of karaoke scores, song-browsing by lyrics, and the generation of audio thumbnails. Existing methods are restricted to using only lyrics and match them to phoneme features extracted from the audio (usually mel-frequency cepstral coefficients). Our novel idea is to integrate the textual chord information provided in the paired chords-lyrics format known from song books and Internet sites into the inference procedure. We propose two novel methods that implement this idea: First, assuming that all chords of a song are known, we extend a hidden Markov model (HMM) framework by including chord changes in the Markov chain and an additional audio feature (chroma) in the emission vector; second, for the more realistic case in which some chord information is missing, we present a method that recovers the missing chord information by exploiting repetition in the song. We conducted experiments with five changing parameters and show that with accuracies of 87.5% and 76.7%, respectively, both methods perform better than the baseline with statistical significance. We introduce the new accompaniment interface Song Prompter, which uses the automatically aligned lyrics to guide musicians through a song. It demonstrates that the automatic alignment is accurate enough to be used in a musical performance.Index Terms-Audio user interfaces, hidden Markov models (HMMs), music, music information retrieval, speech processing.

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.

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity. Index Terms-Music information retrieval (MIR), singer identification, singing voice, vocal, vocal timbre similarity.Hiromasa Fujihara received the B.S. and M.S. degrees from Kyoto University, Kyoto, Japan, in 2005 and 2007, respectively. He is currently pursuing the Ph.D. degree in the Japan. He was a Visiting Scholar at Stanford University, Stanford, CA, from 1986 to 1988. He has done research in programming languages, parallel processing, and reasoning mechanism in AI. He is currently engaged in computational auditory scene analysis, music scene analysis, and robot audition.

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Three techniques are described that improve a previously developed system for automatically synchronizing lyrics with musical audio signals. Although this system achieves state-of-the-art accuracy by extracting vocal vowels from polyphonic sound mixtures and using forced alignment between those vowels and a phoneme network of the lyrics, there was still room for improvement. The first technique detects nonexistence regions in which fricative consonant sounds do not exist, which were not utilized in the previous system, and prohibits the alignment of the fricative phonemes to those regions. The second technique inserts a filler model between phrases of the phoneme network. This model improves the accuracy of the forced alignment by ignoring inter-phrase vowel utterances not included in the lyrics. The third technique introduces novel feature vectors for vocal activity detection that enable a distance calculation between two sets of the harmonic structure without estimating their spectral envelopes. Experimental results showed that all three techniques contribute to improved synchronization.

A new vitrification process for radioiodine has been developed for safe disposal of the spent iodine adsorbent generated from reprocessing off-gas systems. The proposed process consists of the following two steps: (1) separation of radioiodine as AgI from spent iodine adsorbent and (2) vitrification of the separated AgI with silver phosphate. An AgI-Ag2O-P2O5 glass system can homogeneously contain up to 60mol%AgI, which leads to high volume reduction efficiency (approximately 1/25). It also can vitrify the AgI without volatilization of iodine because of its low melting temperature (below 400°C). The leachabilities of iodine from simulated vitrified waste forms were evaluated by the MCC-1 static leach test in an anaerobically controlled glove box, which was purged by nitrogen gas with 3% hydrogen. The leaching behavior of the Agl-Ag2O-P2O5 glass system was influenced by the composition of the actual glass, type of leachant, and redox conditions. When the leach test was carried out using simulated ground wateroriginating from rainfall, the leach rate of iodine from 3AgI-Ag4P2O7 glass was 6×10−8g/cm2/d in the early period of the leach test at 35°C under the reducing condition, after which it decreased to 3×10−9g/cm2/d. This glass showed a lower leach rate in the simulated ground water originating from sea water or cement saturated water, since a precipitation layer was formed on the surface.

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