WEDNESDAY AFTERNOON, 28 NOVEMBER 2007

GRAND COUTEAU, 1:30 TO 5:00 P.M.

Session 2pMU
Musical Acoustics and Speech Communication: Musical Pitch Tracking and Sound Source Separation
Leading to Automatic Music Transcription IIJames W. Beauchamp, Chair
Univ. of Illinois Urbana-Champaign, School of Music, Dept. of Electrical and Computer Engineering, Urbana, IL 61801
Chair’s Introduction—1:30

Invited Papers
1:35
2pMU1. Active music listening interfaces based on sound source separation and F0 estimation. Masataka Goto 共Natl. Inst. ofAdv. Industrial Sci. and Technol. 共AIST兲, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan, m.goto@aist.go.jp兲
This paper describes research aimed at building ‘‘active music listening interfaces’’ to demonstrate the importance of music
understanding technologies, including sound source separation and F0 estimation, and the benefit they offer to end users. Active music
listening is a way of listening to music through active interactions. Given polyphonic sound mixtures taken from available music
recordings, our active music listening interfaces enrich end-users’ music listening experiences. For example, by suppressing drumsounds and adding other drum sounds, Drumix 关Yoshii et al., IPSJ Journal, 48共3兲, 共2007兲兴 enables a user to change the volume andtimbre of drum sounds separated from the mixtures and rearrange rhythmic patterns of these drum sounds during playback. In
addition, during the playback of a song, LyricSynchronizer 关Fujihara et al., Proc. of IEEE ISM 2006兴 displays scrolling lyrics andhighlights the phrase currently sung. Because lyrics are automatically synchronized with vocal melodies 共separated from polyphonicsound mixtures using our predominant-F0 estimation method PreFEst兲, a user can easily follow the current playback position andclick on a word in the lyrics to listen to it. These interfaces can also be regarded as ‘‘augmented music-understanding interfaces’’ thatfacilitate deeper understanding of music by end users.
1:55
2pMU2. Timbre modeling for the purpose of sound source recognition and separation in music. Anssi Klapuri 共Inst. of Signal
Processing, Tampere Univ. of Technol., Korkeakoulunkatu 1, 33720 Tampere, Finland兲Timbre models of musical instruments were studied in order to recognize sound sources in music and to organize sounds to theirsources. The log-power spectrum of the sounds was modeled using a combination of linear bases, one defined as a function offrequency, and another as a function of harmonic index. Algorithms for initializing, learning, and sequentilly adapting this model aredescribed. In simulations, the model had a benefit over conventional Mel-cepstral representations, especially in the case of certain
instruments, including the electric guitar, the organ, the mallet percussions, and the clarinets. The improvement was observed insupervised instrument classification, but not so clearly in the unsupervised clustering of sounds.
2:15
2pMU3. Supervised separation of musical sources. Paris Smaragdis 共Mitsubishi Electric Res. Labs, 201 Broadway, 8th Fl.,
Cambridge, MA 02139, paris@media.mit.edu兲
Sounds, especially in the musical context, are often presented in mixtures. Given that traditional signal processing theory is notwell equipped to work with concurrent sound sources, this limits the analysis and creative options on musical audio. As it was recentlyshown, sound mixtures can be analyzed with latent variable models of time-frequency distributions and, thus, reveal the additive
structure of acoustic scenes. Unfortunately, such processes separate acoustic elements in an indiscriminant manner that does notalways result in extracting the desired sources. However, in the case where a user can provide examples of the sources to extract, theaforementioned algorithms can become powerful tools for supervised source separation. Performing experiments on real singlechannel recordings, it was shown that by simply indicating the regions of time where a particular source was active, it is possible toextract that source with minimal distortion, even though it may constantly be part of a sound mixture. Since this is an example-based
procedure, it is able to deal with arbitrary sources, regardless of their acoustic properties, without requiring any tedious heuristicmodeling. This extraction method facilitates the analysis of musical data, and also allows creative manipulation of music.
2:35
2pMU4. A framework for sound source separation using spectral clustering. George Tzanetakis, Mathieu Lagrange, Luis
Gustavo Martins, and Jennifer Murdoch 共Dept. of Comput. Sci., Univ. of Victoria, Canada, gtzan@cs.uvic.ca兲
Clustering based on the normalized cut criterion, and more generally, spectral clustering methods, are techniques originallyproposed to model perceptual grouping tasks, such as image segmentation in computer vision. In this work, it is shown how suchtechniques can be applied to the problem of dominant melodic source separation in polyphonic music audio signals. One of the main
advantages of this approach is the ability to incorporate mutiple perceptually-inspired grouping criteria into a single framework
without requiring multiple processing stages, as many existing computational auditory science analysis approaches do. Experimental
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analysis front-end. A novel similarity cue based on harmonicity 共harmonically-wrapped peak similariy兲 is also introduced. The
proposed system is data-driven 共i.e., requires no prior knowledge about the extracted source兲, causal, robust, practical, and efficient
共close to real-time on a fast computer兲. Although a specific implementation is presented, one of the main advantages of the proposed
approach is its ability to utilize different analysis front-ends and grouping criteria in a straightforward manner.

2:55
2pMU5. Methods for stereo music source separation. Andreas Ehmann 共Dept. of Elec. and Comput. Eng., Univ. of Illinois at
Urbana-Champaign, 1406 W. Green St., Urbana, IL 61801兲
Musical source separation from stereo signals has been growing in popularity recently. Algorithms 共including the author’s兲 and theresults of a recent stereo audio source separation evaluation campaign 共SASSEC兲 will be presented. In general, such approaches
involve extracting mixing parameters from time-frequency 共T-F兲 analyses of both channels. Each corresponding T-F point of the twochannels is compared and interchannel intensity differences 共IID兲 and 共in the case of stereo mixing兲, interchannel phase differences
共IPD兲 are computed. A histogram of signal power in the IID and IPD space is constructed, with sources showing strong peaks at thelocations of their mixing parameters. Masks are then created for each potential source, corresponding to T-F points that share common
mixing parameters. These masks are applied to the T-F analysis and resynthesized as probable sources. The underlying assumption ofsuch systems is that of W-disjoint orthogonality, that is, T-F points of the sources do not overlap. In music, however, this is rarely thecase, as common musical intervals often contain overalapping harmonic ‘‘collisions.’’ The use of an exponentially damped sinusoidalmodel, that in limited cases has the ability to resolve very closely spaced, beating, sinusoids is explored as a means of separating
harmonic collisions.
Break

2p WED. PM

3:15–3:25

3:25
2pMU6. Algorithm for separating vocals from polyphonic music. Tuomas Virtanen and Matti Ryynänen 共Tampere Univ. ofTechnol., P.O. Box 553, FI-33101 Tampere, Finland, tuomas.virtanen@tut.fi兲An algorithm for the separation of vocals from polyphonic music is described. The algorithm consists of two stages, which firstestimate the predominant melody line, and then the sinusoidal modeling parameters corresponding to the melody line. The melody line
is estimated using a hidden Markov model where the output of a multiple fundamental frequency estimator is used as a feature set. The
states of the hidden Markov model correspond to musical notes having different fundamental frequencies, and the state transition
probabilities are determined by a musicological model. The sinusoidal modeling stage estimates the frequency, amplitude, and phaseof each overtone of the predominant melody line in each frame. The sinusoidal modeling stage can also include mechanisms which
reduce the effect of interfering sound sources on the estimated parameters. The resulting separation algorithm is independent of singer
identity, musical genre, or instrumentation. Simulation experiments on real polyphonic music show that the algorithm enables
separation of vocals from the accompaniment, providing robust results on various musical genres.

3:45
2pMU7. Separation of singing voice from music accompaniment for monaural recordings. Yipeng Li and DeLiang Wang
共Dept. of Comput. Sci. and Eng., The Ohio State Univ., Columbus, OH 43210兲
Separating singing voice from music accompaniment is useful in a wide range of applications, such as lyrics recognition andalignment, singer identification, and music information retrieval. Compared to speech separation, which has been extensively studied
for decades, singing voice separation has been little explored. We have developed a pitch-based system to separate singing voice frommusic accompaniment for monaural recordings. Our system consists of three stages. The singing voice detection stage partitions andclassifies an input into vocal and nonvocal portions. For each vocal portion, the predominant pitch detection stage detects the pitchesof the singing voice. Finally, the separation stage uses the detected pitch to group the time-frequency segments of the singing voice.
Quantitative results show that the system performs the separation task successfully. 关The work is supported by AFOSR and AFRL.兴

4:05
2pMU8. Statistical models for music signal analysis and transcription. A. Taylan Cemgil 共Dept. of Eng., Univ. of Cambridge,
Trumpington St., CB2 1PZ Cambridge, UK兲
In recent years, there has been an increasing interest in statistical approaches and tools from machine learning, for the analysis ofaudio and music signals. The application of statistical techniques is quite natural: Acoustical time series can be conveniently modeledusing hierarchical signal models by incorporating prior knowledge from various sources: From physics or studies of human cognitionand perception. Once a realistic hierarchical model is constructed, many audio processing tasks, such as coding, restoration, transcription, separation, identification, or resynthesis can be formulated consistently as Bayesian posterior inference problems. This
contribution illustrates various realistic generative signal models for audio and music signal analysis. In particular, factorial switchingstate space models, Gamma-Markov random fields, and point process models will be discussed. Some models admit exact inference,otherwise, efficient algorithms based on variational or stochastic approximation methods can be developed. We will illustrate theapproach on music transcription, restoration, and source separation applications. 关Work supported by EPSRC.兴
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4:25
2pMU9. Approaching polyphonic transcription of piano sounds. Luis I. Ortiz-Berenguer 共Audiovisual and Commun. Eng. Dept.,
EUITT, Polytechnic Univ. of Madrid 共UPM兲, Ctra Valencia km7, Madrid, Spain兲 and Francisco J. Casajus-Quiros 共Polytechnic Univ.
of Madrid 共UPM兲, Ciudad Universitaria s/n. Madrid, Spain兲Polyphonic transcription of piano is a challenging task due to the specific characteristics of its sound spectrum. Pattern matching
method, which compares the spectrum with a set of spectral patterns, has proven to give good results, although some limitations still
exist mainly when analyzing notes of the lower octaves. Present research is oriented to improve both the accuracy of the spectral
patterns, especially for lower octaves, and the matching-score calculation. The former is being carried out by developing a model ofthe vibration of piano. The last is aimed to decide whether a simple internal product or a more complex MSE calculation are needed
and how the spectral pattern has to be modeled regarding partial levels. The model of the vibration permits calculating the partial
frequencies, for which it takes into account the vibration of the stiff string and the effect of the soundboard impedance. Thesoundboard impedance modeling is under a deeper study using both real measurements and FEM modeling. As further work, once themodel is completed, it is expected to generate the patterns by using a waveguide synthesis method. 关Work supported by Spanish
National Project TEC2006-13067-C03-01/TCM.兴

Contributed Paper
4:45

analytical signal representation of the output of a set of adaptive band-passfilters that perform initial spectral separation. A small number of cleanly
separable partials, usually the fundamentals, are then employed as references to which the remaining filter-bank outputs are correlated. To separate overlapping spectral features and ascribe the proper amount of power
to each source, the phase of the reference signal is scaled to the harmonicfrequency, and the amplitude of each overtone is estimated by computing
the cross-correlation of the phase trajectories. To group sonic events intime, we employ the history of the signal itself, thus eliminating the needfor prior information. We discuss this separation method and the audio
quality of the reconstructed source signals, with examples and a quantitative perceptual metric.

2pMU10. Employing the mutual phase coherence of the overtones inmusical sounds for audio source separation and reconstruction.
Gordana Velikic and Mark F. Bocko 共Dept. of Elec. and Comput. Eng.,
Univ. of Rochester, Rochester, NY 14627兲
Source separation from a monaural mixture of musical sounds is challenging. When only a single mixture of multiple sources is available, themethod of independent component analysis may not be straightforwardly
applied, and proposed source separation methods have relied on previous
knowledge of the independent sources. Our method employs mutual correlations of the overtone phases of musical sounds to ascribe spectralfeatures to individual sources. The phase versus time is computed from theWEDNESDAY AFTERNOON, 28 NOVEMBER 2007

GRAND BALLROOM E, 3:00 TO 4:40 P.M.

Session 2pNS
Noise: Rain Noise
Robert C. Coffeen, Chair
Univ. of Kansas, School of Architecture and Urban Design, Lawrence, KS 66045
Chair’s Introduction—3:00

Invited Papers
3:05
2pNS1. A standard way of measuring rainfall noise. Richard J. Peppin 共Scantek, Inc., 7060 Oakland Mills Rd., #L, Columbia,MD 20146兲 and Robert C. Coffeen 共Univ. of Kansas, Lawrence, KS 66045兲
About 10 years ago at an ISO TC 29 meeting, the concept of the measurement of rainfall noise was suggested. At the time it madeno sense to me. Who cares about this? Not me, since I liked the sound of rain on my roof, and the louder the better. The concept wasto evaluate roofs to assess the insulation capabilities from sound 共vibration?兲 produced by rainfall. But in spite of my skepticism, wiser
heads prevailed, and in the end, the approach was drafted into a work item, a draft, and a final draft standard. This paper is a summaryof ISO FDIS 140-18, ‘‘Acoustics—Measurement of sound insulation in buildings and of building elements—part 18: Laboratory
measurement of sound generated by rainfall on building elements.’’ I discuss the equipment needed, the test method, and theuncertainty.
3:25
2pNS2. Rain noise level measurements in a performing arts center. Robert C. Coffeen 共School of Architecture and Urban
Planning, Univ. of Kansas, 1465 Jayhawk Blvd., Lawrence, KS 66045兲
Noise level measurements made in a performance hall and in related spaces with subjectively described light, moderate, and heavy
rainfall on the facility roof are presented in octave frequency bands, and as described by associated A weighted sound pressure levels
and noise criteria 共NC兲 ratings. Rain noise levels are also compared with HVAC noise and ambient noise without the HVAC system
operating.
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