Page  00000001 High-frequency compensation of low sample-rate audio files: A wavelet-based spectral excitation algorithm Corey I. Cheng coreyc @ Department of Electrical Engineering and Computer Science University of Michigan, Ann Arbor Abstract The present work documents the design and implementation of a wavelet-based excitation algorithm for low sample-rate audio files. This algorithm is an analysis/resynthesis method which converts a low sample-rate audio file to a higher sample-rate audio file, while inserting high frequency content in the wavelet domain. This paper presents relevant wavelet theory, along with the theory of the excitation algorithm and some sonic results. We find that the excitation algorithm works reasonably well for percussive, transient-oriented sounds, but fails for highly-resonant steady-state sounds, e.g., vocalic segments of speech. 1 Introduction and motivation Although the high frequency range of a typical audio recording has little power compared with its lower frequencies, much information, such as localization and ambient cues, is contained in the higher frequencies. Unfortunately, an audio signal's high frequency content may have been attenuated during various recording or processing stages. The various systems that have been introduced to accomplish high-frequency boosting and compensation are called "exciters" in the audio industry, and generally work by emphasizing a signal's existing but attenuated high-frequency content. However, in a heavily noisecorrupted signal or a low-sample-rate digital audio signal, the high frequencies for which we might wish to compensate might be absent altogether. In this case we face a far more acute problem, as we must "guess" or extrapolate this high frequency content from the existing lower frequency content. Wavelets are well suited to this predictive task. Since a dyadic wavelet analysis/resynthesis is an octave-band decomposition that can be done in linear time, an entire octave of high frequency information can be extrapolated from existing lower frequencies more efficiently than a corresponding Fourier technique. Furthermore, as is shown below, an exponential decay property of wavelet coefficients allows us to extrapolate high frequencies from a low sample-rate audio signal in a straightforward manner [14]. 2 Relevant wavelet theory Wavelet analysis is one of many generalized timefrequency methods which serves to describe a signal's frequency content at a certain point in time. As opposed to Fourier analysis, wavelet analysis divides the timefrequency plane into non-uniform regions which are characteristic of an octave-band decomposition. Each region of the time-frequency plane is associated with a time-domain signal called a wavelet. These wavelets form a basis for the analyzed signal, and, when added together in prescribed amounts, can perfectly reconstruct certain classes of signals. In addition, a defining feature of wavelet analysis is that all of these time domain signals share the same general shape, and that in fact, they only differ by compressions and expansions in time by powers of two. A level-n dyadic wavelet transform produces n levels of wavelet coefficients and a final average coefficient, which serve to summarize in the wavelet domain the frequency content of the signal at a certain time. As shown in Figure 1, a higher-level wavelet coefficient corresponds to higher frequency content, wider frequency range, and a smaller time interval. The forward and inverse dyadic wavelet transform of a window of samples can be implemented using a set of upsamplers, downsamplers, and recursive two-channel digital filter banks. The coefficients of each of the filters in the filterbank are directly related to both the timedomain shape of the wavelets, as well as the frequency response of the wavelet analysis. For the forward wavelet transform, the output of the filter banks are the wavelet coefficients at a desired level of resolution; for

Page  00000002 A possible three-level wavelet analysis and its non-uniform division of the time-frequency plane. Each rectangle represents one wavelet coefficient. The darker the rectangle's shading, the larger the magnitude of the wavelet coefficient. Here sr is the sampling rate. Level 3 wavelet ___ Sr/2_____ coefficients Freq. (Hz).^^::::::::: |||j_ Level 2 wavelet sr/4 1111111i coefficients sr/8.i Level 1 wavelet coefficients 0 4 8 12 \ Final average I coefficient Time (sec. X.01) Figure 1 Forward ------ low -2 final average l w p as s 1 2 lev el 1 w a v e let low u 1high coefficients input pass 1 i pass 1 samle 1 2 level 2 wavelet high a coefficients as 1level 3 wavelet coefficients Inverse Transform final average level 1 wavelet coefficients level 2 wavelet coefficients level 3 wavelet coefficients reconstructed low input samples pass 2 low high 2 pass 2 low 2 pass 2 pass 2 high 2 pass2 whose coefficients are symmetric with respect to a central axis, as we wish for no phase distortion to result from the filtering. Therefore, the wavelets and filters used in the excitation algorithm come from a family known as "binlets," which are biorthogonal, symmetric, binary coefficient filters. These wavelets and filters were chosen for their symmetry and simplicity, and are specified for low orders by Strang [14]. 3 Excitation algorithm Choose a set of binlet analysis and resynthesis filters having p vanishing moments, where the audio signal to be excited is assumed to be roughly piecewise polynomial of degree p-1, and therefore smooth in this sense. Using the selected binlet analysis / resyhtnesis filters, perform an nth-level, dyadic, forward wavelet transform of the audio signal. Next, add a (n+1)th level of wavelet coefficients to the existing wavelet analysis, one level above the most detailed level of the existing analysis. This will effectively double the frequency.range and the amount of data in the analysis. We now note that Strang [14] states that the magnitude of wavelet coefficients decays exponentially across analysis levels, and that the rate of decay is related to the smoothness of the analyzed function and the wavelet analysis level: (1) Iff (t) has p derivatives, its wavelet coefficients at levelj decay like 2jp, wherej is the wavelet analysis level, and |bjk = f(t)wjk(t) dt ~C2 f"(P)(t), where bjk is the wavelet coefficient at levelj, translation k, wjk is the wavelet basis function associated with level j, translation k, and C e - Consequently, extrapolate the (n+l)th level wavelet coefficients' values from existing wavelet coefficients by assuming that the new coefficients are exponentially related to the previous levels' values. For example, in a five-level analysis, the guessed coefficient is a sixthlevel coefficient which is equal to: (2) b6= -2b5 + 2-2b4 + 2-3Pb3 + 2-4Pb2 + 2"5b, + 2+-6Pb, where b, is the " appropriate" wavelet coefficient at level n, and p is the number of vanishing moments in the analyzing and synthesizing wavelets Equation (2) can be generalized into a formula which allows the guessed wavelet coefficient values to rely on fewer levels of wavelet coefficients than are present in the original analysis [4]: Figure 2 the inverse wavelet transform, the wavelet coefficients are the input, and the output of the final set of filter banks is the reconstructed window. The construction of the four digital filters in Figure 2 is not a trivial task, as the filters must satisfy the "dilation equation" and several other mathematical restrictions, including non-distortion and non-aliasing requirements. In particular, developments in wavelet theory have established that requiring the low-pass filters in Figure 2 to have p zeros at 71 allows the filter to perfectly analyze and resynthesize a polynomial of degree p-1. Specifically, a filter that has p zeros at 71 is said to have p vanishing moments. Therefore, loosely speaking, the more vanishing moments a filter has, the more "jagged" a signal it can analyze and perfectly reconstruct. There are many wavelet families whose analyzing and resynthesizing filter coefficients have already been derived, such as the Haar and Daubechies filters [7]. However, in the present context, we require a filter set

Page  00000003 Adding another level of wavelet coefficients to an existing analysis to increase the frequency bandwidth of a low-sample-rate audio file Coefficient information is doubled, and the sample rate of the resulting file is also doubled to provide "headroom" for the new, higher frequency data. A guessed coefficient value is determined from an exponentially decaying reliance on the coefficients that fall directly below it. new level of wavelet coefficients, guessed values - frequency (analysis level) For example, if the analysis is done with an analysis and synthesis wavelet with p vanishing moments, this guessed wavelet coefficient is equal to... I- 2-P times this value 2-2p times this value S2-3p times this value I+1 i- 2-4p times this value I+1 c- 2-'p times this value |time (translation) I The number of levels upon which the guess depends can be chosen; typically, the more the dependence on lower levels, the more aliasing tends to occur. Most successful results were found when the guess only relied on one level: the level right beneath it. Figure 3 N 1 (3) b1, = 2pi+1 b where,;=0 2z - +1),[1/2 b,, is the wavelet coefficient at level s translation t N is the number of existing wavelet analysis levels from which to extrapolate p is the number of vanishing moments in the synthesizing and analyzing wavelets 1 is the translation of the guessed wavelet coefficient. I is the total number of levels in the original analysis [ ]represents the least greatest integer (stair) function Finally, perform an (n+l)th level inverse wavelet transform on the n+1 levels of wavelet coefficients, normalize the result to account for power loss, and double the sampling rate associated with the data to account for the doubling of the amount of data. 4 Experiments and results Original 44.1 kHz, stereo, 16-bit AIFF files were downsampled to 11.025 kHz in order to eliminate high frequency information in the process. The signals were then spectrally enhanced twice to restore the sampling rate to 44.1kHz. The sound quality of the original signal and the spectrally enhanced file were then qualitatively compared. A window of 32768 frames was used for each example, and 2 iterations of the wavelet transform were performed for each lower resolution file. Best results were obtained when the guessed wavelet coefficients only relied on one level of previous wavelet coefficients (N=1 in (3)). The 6/10 binarycoefficient wavelet analysis/ resynthesis filters were used (p=3 in (1)), and the spectrally enhanced signals were normalized to account for power loss, so that comparisons could be made to the original 44.1 kHz signal.

Page  00000004 The comparisons were best for drum sounds and high-pitched sounds, acceptable for heavy percussion sounds, and poor for speech sounds. This is consistent our expectations, since 1) vocalic speech is mostly contained in low-frequency regions, and therefore enhancement essentially adds high frequency material that is not present in the original signal, and 2) wavelet analysis is wellsuited to many types of transient analysis. Imaging was much improved in most cases, and ambiance was added in many cases as well. However, there was a grainy sound to the processed output, as the filter coefficients used did not have sharp rolloffs nor was any pre-processing done to the signal before performing the wavelet decomposition. Unfortunately, the boundary artifacts associated with this windowed process were also audible. 5 Conclusions and future directions The results from this wavelet-based process are promising: frequency information has been added to low-sample-rate soundfiles that sound convincing in many cases. However, the algorithm presented here is only a first step which requires refinement in several ways: the power attenuation problem must be fixed; existing coefficients could also be altered to provide for a better result, different, higher-order filters customized for audio analysis could be used, and the boundary artifacts should be removed. 6 Acknowledgments The author would like to thank Professor Gregory H. Wakefield from the EECS Department at the University of Michigan for his help and encouragement in preparing this manuscript for publication. In addition, the author would like to thank the faculty and graduate students at the Bregman Electro-acoustic Music studios at Dartmouth College, where much of this research was done. References [1] Arfib, D., and Delprat, N. "Musical Transformations Using the Modification of Time-Frequency Images." Computer Music Journal, 17:2. The MIT Press, Cambridge, MA: 1993. [2] Berger, J. and Nichols, C. "Using Wavelet Based Analysis and Resynthesis to Uncover the Past." Proc. 1994 ICMC, ICMA, San Francisco: 1994. [3] Chan, Y.T. Wavelet Basics. Kluwer Academic Publishers, Boston, MA: 1995. [4] Cheng, C. Wavelet Signal Processing of Digital Audio with Applications in ElectroAcoustic Music. Dartmouth College Masters' Thesis, Dartmouth College, Hanover, New Hampshire: 1996. [5] Chui, C. K. et al. Wavelets: Theory, Algorithms, and Applications. Academic Press, Inc., Boston, MA: 1994. [6] Cohen, A. and Daubechies, I. "Biorthogonal Bases of Compactly Supported Wavelets." Communications on Pure and Applied Mathematics, 45:485-560, 1992. [7] Daubechies, I. "Orthonormal Bases of Compactly Supported Wavelets." Communications on Pure and Applied Mathematics, 41:7. John Wiley & Sons, New York: 1988. [8] De Poli, G. et al., eds. Representations of Musical Signals. The MIT Press, Cambridge, Massachusetts: 1991. [9] Guillemain, Ph. and Kronland-Martinet, R. "Additive resynthesis of sounds using continuous time-frequency analysis techniques." Proc. 1992 ICMC, ICMA, San Francisco: 1992. [10] Kronland-Martinet, R. "The Wavelet Transform for Analysis, Synthesis, and Processing of Speech and Music Sounds." Computer Music Journal, 12:4. The MIT Press, Cambridge, MA: 1988. [11] Kussmaul, C. "Applications of the Wavelet Transform at the Level of Pitch Contour." Proc. 1991 ICMC, ICMA, San Francisco: 1991. [12] Kussmaul, C. Applications of Wavelets in Music: The Wavelet Function Library. Dartmouth College Masters' Thesis, Dartmouth College, Hanover, New Hampshire: 1991. [13] Popovic, I. et al. "Aspects of Pitch-Tracking and Timbre Separation: Feature Detection in Digital Audio Using Adapted Local Trigonometric Bases and Wavelet Packets." Proc. 1995 ICMC, ICMA, San Francisco: 1995. [14] Strang, G. and Nguyen, T. Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA: 1996.