One Microphone Source Separation

Source separation, or computational auditory scene analysis , attempts to extract individual acoustic objects from input which contains a mixture of sounds from different sources, altered by the acoustic environment. Unmixing algorithms such as lCA and its extensions recover sources by reweighting multiple observation sequences, and thus cannot operate when only a single observation signal is available. I present a technique called refiltering which recovers sources by a nonstationary reweighting ("masking") of frequency sub-bands from a single recording, and argue for the application of statistical algorithms to learning this masking function . I present results of a simple factorial HMM system which learns on recordings of single speakers and can then separate mixtures using only one observation signal by computing the masking function and then refiltering. 1 Learning from data in computational auditory scene analysis Imagine listening to many pianos being played simultaneously. If each pianist were striking keys randomly it would be very difficult to tell which note came from which piano. But if each were playing a coherent song, separation would be much easier because of the structure of music. Now imagine teaching a computer to do the separation by showing it many musical scores as "training data". Typical auditory perceptual input contains a mixture of sounds from different sources, altered by the acoustic environment. Any biological or artificial hearing system must extract individual acoustic objects or streams in order to do successful localization, denoising and recognition. Bregman [1] called this process auditory scene analysis in analogy to vision. Source separation, or computational auditory scene analysis (CASA) is the practical realization of this problem via computer analysis of microphone recordings and is very similar to the musical task described above. It has been investigated by research groups with different emphases. The CASA community have focused on both multiple and single microphone source separation problems under highly realistic acoustic conditions, but have used almost exclusively hand designed systems which include substantial knowledge of the human auditory system and its psychophysical characteristics (e.g. [2,3]). Unfortunately, it is difficult to incorporate large amounts of detailed statistical knowledge about the problem into such an approach. On the other hand, machine learning researchers, especially those working on independent components analysis (lCA) and related algorithms, have focused on the case of multiple microphones in simplified mixing environments and have used powerful "blind" statistical techniques. These "unmixing" algorithms (even those which attempt to recover more sources than signals) cannot operate on single recordings. Furthermore, since they often depend only on the joint amplitude histogram of the observations they can be very sensitive to the details of filtering and reverberation in the environment. The goal of this paper is to bring together the robust representations of CAS A and methods which learn from data to solve a restricted version of the source separation problem isolating acoustic objects from only a single microphone recording. 2 Refiltering vs. unmixing Unmixing algorithms reweight multiple simultaneous recordings mk (t) (generically called microphones) to form a new source object s(t): s(t) = D:lml(t)+D:2m2(t)+ ... +D:KmK(t) (1) '-v-" '-v-'" '-v-'" ............... estimated source mic 1 mic 2 mic K The unmixing coefficients D:i are constant over time and are chosen to optimize some property of the set of recovered sources, which often translates into a kurtosis measure on the joint amplitude histogram of the microphones . The intuition is that unmixing algorithms are finding spikes (or dents for low kurtosis sources) in the marginal amplitude histogram. The time ordering of the datapoints is often irrelevant. Unmixing depends on a fine timescale, sample-by-sample comparison of several observation signals. Humans, on the other hand, cannot hear histogram spikes l and perform well on many monaural separation tasks. We are doing structural analysis, or a kind of perceptual grouping on the incoming sound. But what is being grouped? There is substantial evidence that the energy across time in different frequency bands can carry relatively independent information. This suggests that the appropriate subparts of an audio signal may be narrow frequency bands over short times. To generate these parts, one can perform multi band analysis break the original signal y(t) into many subband signals bi(t) each filtered to contain only energy from a small portion of the spectrum. The results of such an analysis are often displayed as a spectrogram which shows energy (using colour or grayscale) as a function of time (ordinate) and frequency (abscissa). (For example one is shown on the top left of figure 5.) In the musical analogy, a spectrogram is like a musical score in which the colour or grey level of the each note tells you how hard to hit the piano key. The basic idea of refiltering is to construct new sources by selectively reweighting the multiband signals bi(t). Crucially, however, the mixing coefficients are no longer constant over time; they are now called masking signals. Given a set of masking signals, denoted D:i(t), a source s(t) can be recovered by modulating the corresponding subband signals from the original input and summing: s(t) '-v-" estimated source mask 1 ,-.-.. D:l(t) b1(t) ~ sub-band 1 mask 2 ,-.-.. + D:2(t) b2(t) ~ sub-band 2 maskK ,.--.... + ... + D:K(t) bK(t) '-v-" sub-band K (2) The D:i(t) are gain knobs on each subband that we can twist over time to bring bands in and out of the source as needed. This performs masking on the original spectrogram. (An equivalent operation can be performed in the frequency domain.2) This approach, illustrated in figure 1, forms the basis of many CASA approaches (e.g. [2,3,4]). For any specific choice of masking signals D:i(t), refiltering attempts to isolate a single source from the input signal and suppress all other sources and background noises. Different sources can be isolated by choosing different masking signals. Henceforth, I will make a strong simplifying assumption that D:i(t) are binary and constant over a timescale T of roughly 30ms. This is physically unrealistic, because the energy in each small region of time-frequency never comes entirely from a single source. However in practice, for small numbers of sources, this approximation works quite well (figure 3). (Think of ignoring collisions by assuming separate piano players do not often hit the same note at the same time.) lTry randomJy permuting the time order of samples in a stereo mixture containing several sources and see if you still hear distinct streams when you play it back. 2Make a conventional spectrogram of the original signal y(t) and modulate the magnitude of each short time DFT while preserving its phase: SW(T) = F1 {D:wIIF{yW(r)}IILF{yW(r)}} where sW(r) and yW(r) are the wth windows (blocks) of the recovered and original signals, oi is the masking signal for subband i in window w, and F[·] is the DFf. Figure 1: The refiltering approach to one microphone source separation. Multiband analysis of the original signal y(t) gives sub-band signals bi(t) which are modulated by masking signals ai(t) (binary or real valued between 0 and 1) and recombined to give the estimated source or object s(t). Refiltering can also be thought of as a highly nonstationary Wiener filter in which both the signal and noise spectra are re-estimated at a rate l/T; the binary assumption is equivalent to assuming that over a timescale T the signal and noise spectra are nonoverlapping. It is a fortunate empirical fact that refiltering, even with binary masking signals, can cleanly separate sources from a single mixed recording. This can be demonstrated by taking several isolated sources or noises and mixing them in a controlled way. Since the original components are known, an "optimal" set of masking signals can be computed. For example, we might set 0i ( t) equal to the ratio of energy from one source in band i around times t ± T to the sum of energies from all sources in the same band at that time (as recommended by the Wiener filter) or to a binary version which thresholds this ratio. Constructing masks in this way is also useful for generating labeled training data, as discussed below. 3 Multiband grouping as a statistical pattern recognition problem Since one-microphone source separation using refiltering is possible if the masking signals are well chosen, the essential problem becomes: how can the Oi(t) be computed automatically from a single mixed recording? The goal is to group or "tag" together regions of the spectrogram that belong to the same auditory object. Fortunately, in audition (as in vision), natural signals-especially speech---exhibit a lot of regularity in the way energy is distributed across the time-frequency plane. Grouping cues based on these regularities have been studied for many years by psychophysicists and are hand built into many CASA systems. Cues are based on the idea of suspicious coincidences: roughly, "things that move together likely belong together". Thus, frequencies which exhibit common onsets, offsets, or upward/downward sweeps are more likely to be grouped into the same stream (figure 2). Also, many real world sounds have harmonic spectra; so frequencies which lie exactly on a harmonic "stack" are often perceptually grouped together. (Musically, piano players do not hit keys randomly, but instead use chords and repeated melodies.) Harmonic stacking. Common onset. Frequency co-modulation. Figure 2: Examples of three common grouping cues for energy which often comes from a single source. (left) Frequencies which lie exactly on harmonic multiples of a single base frequency. (middle) Frequencies which sudd