Restoration and Enhancement of Solo Guitar Recordings based on Sound Source Modeling

Restoration and Enhancement of Solo Guitar Recordings

Based on Sound Source Modeling

Paulo A. A. Esquef, Vesa Välimäki, and Matti Karjalainen

Companion webpage with sound examples to the homonym paper published in the Journal of the Audio Engineering Society, April, 2002.

1. Introduction

This page presents sound examples of audio restoration and enhancement based on Sound Source Modeling (SSM). We present a case based on the commuted waveguide synthesis algorithm for plucked strings, in particular, for single acoustic guitar tones. Comparisons with traditional approaches are also provided.

2. SSM and Enhancement

Enhancement here is related to extending the bandwidth of the signal. We start with a lowpass filtered version of the signal and use a SSM-based scheme to recreate the missing spectral information. The basic scheme consists of estimating suitable model parameters from the degraded signal, obtaining the corresponding excitation via inverse filtering, and using a processed version of the excitation to resynthesize a signal in which the lost information is restored.

Original: original.wav

Lowpass filtered version: lpf1000Hz.wav

Enhanced versions:

1) Noise burst with flat spectrum, SNR 20 dB: enhanced1.wav

2) Noise burst with colored spectrum, SNR 40 dB: enhanced2.wav

3) Noise burst with colored spectrum, SNR 20 dB: enhanced3.wav

4) Noise burst with colored spectrum, SNR 10 dB: enhanced4.wav

2. SSM and Audio De-hissing

.We are considering a case in which the signal to be analyzed is corrupted by zero mean Gaussian white noise.

Test Signals:

Original guitar tone: original.wav

Original + white noise (SNR=10dB): ogn10.wav

2.1. Signal Modeling Approach

Typical problems with spectral-based de-hissing methods are related to the trade-off between the musical noise and the smoothing effects perceived in the restored signal.

Wiener Filtering (normal noise variance estimate): ogn10r1.wav

Wiener Filtering (overestimated noise variance): ogn10r2.wav

In the first case, a residual noise is clearly perceived, while in the second the restored tone is too smoothed.

2.2 Source Modeling Approach

Aggressive de-hissing the signal and then applying the bandwidth extension procedure

Aggressively de-hissed (overestimated noise variance): ogn10adg30.wav

Bandwidth extended after 1.: ogn10adg30be.wav

Integrated de-hissing and bandwidth extension

Aggressively de-hissed excitation (attack part preserved): ogn10ade.wav

This URL: http://www.acoustics.hut.fi/publications/papers/jaes-ssm-ssm/
Last modified: 29.11.2001
Author: <esquef@acoustics.hut.fi>