This paper presents an analysis-manipulation method that can generate musical instrument sounds with arbitrary pitches and durations from the sound of a given musical instrument (called seed) without distorting its timbral characteristics. Based on psychoacoustical knowledge of the auditory effects of timbres, we defined timbral features based on the spectrogram of the sound of a musical instrument as (i) the relative amplitudes of the harmonic peaks, (ii) the distribution of the inharmonic component, and (iii) temporal envelopes. First, to analyze the timbral features of a seed, it was separated into harmonic and inharmonic components using Itoyama’s integrated model. For pitch manipulation, we took into account the pitch-dependency of features (i) and (ii). We predicted the values of each feature by using a cubic polynomial that approximated the distribution of these features over pitches. To manipulate duration, we focused on preserving feature (iii) in the attack and decay duration of a seed. Therefore, only steady durations were expanded or shrunk. In addition, we propose a method for reproducing the properties of vibrato. Experimental results demonstrated the quality of the synthesized sounds produced using our method. The spectral and MFCC distances between the synthesized sounds and actual sounds of 32 instruments were reduced by 64.70% and 32.31%, respectively.

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We describe a novel Query-by-Example (QBE) approach in Music Information Retrieval, which allows a user to customize query examples by directly modifying the volume of different instrument parts. The underlying hypothesis is that the musical genre shifts (changes) in relation to the volume balance of different instruments. On the basis of this hypothesis, we aim to clarify the relationship between the change of the volume balance of a query and the shift in the musical genre of retrieved similar pieces, and thus help instruct a user in generating alternative queries without choosing other pieces. Our QBE system first separates all instrument parts from the audio signal of a piece with the help of its musical score, and then lets a user remix those parts to change acoustic features that represent musical mood of the piece. The distribution of those features is modeled by the Gaussian Mixture Model for each musical piece, and the Earth Movers Distance between mixtures of different pieces is used as the degree of their mood similarity. Experimental results showed that the shift was actually caused by the volume change of vocal, guitar, and drums.

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This paper describes a singing-to-speaking synthesis system called “SpeakBySinging” that can synthesize a speaking voice from an input singing voice and the song lyrics. The system controls three acoustic features that determine the difference between speaking and singing voices: the fundamental frequency (F0), phoneme duration, and power (volume). By changing these features of a singing voice, the system synthesizes a speaking voice while retaining the timbre of the singing voice. The system first analyzes the singing voice to extract the F0 contour, the duration of each phoneme of the lyrics, and the power. These features are then converted to target values that are obtained by feeding the lyrics into a traditional text-to-speech (TTS) system. The system finally generates a speaking voice that preserves the timbre of the singing voice but has speech-like features. Experimental results show that SpeakBySinging can convert singing voices into speaking voices whose timbre is almost the same as the original singing voices.

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This paper describes a new onset-informed source separation method based on non-negative matrix factorization (NMF) with binary masks. Many previous approaches to separate a target instrument sound from polyphonic music have used side-information of the target that is time-consuming to prepare. The proposed method leverages the onsets of the target instrument sound to facilitate separation. Onsets are useful information that users can easily generate by tapping while listening to the target in music. To utilize onsets in NMF-based sound source separation, we introduce binary masks that represent on/off states of the target sound. Binary masks are formulated as Markov chains based on continuity of musical instrument sound. Owing to the binary masks, onsets can be handled as a time frame in which the binary masks change from off to on state. The proposed model is inferred by Gibbs sampling, in which the target sound source can be sampled efficiently by using its onsets. We conducted experiments to separate the target melody instrument from recorded polyphonic music. Separation results showed about 2 to 10 dB improvement in target source to residual noise ratio compared to the polyphonic sound. When some onsets were missed or deviated, the method is still effective for target sound source separation.

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