Detection of random alterations to time-varying musical instrument spectra

Abstract

The time-varying spectra of eight musical instrument sounds were randomly altered by a time-invariant process to determine how detection of spectral alteration varies with degree of alteration, instrument, musical experience, and spectral variation. Sounds were resynthesized with centroids equalized to the original sounds, with frequencies harmonically flattened, and with average spectral error levels of 8%, 16%, 24%, 32%, and 48%. Listeners were asked to discriminate the randomly altered sounds from reference sounds resynthesized from the original data. For all eight instruments, discrimination was very good for the 32% and 48% error levels, moderate for the 16% and 24% error levels, and poor for the 8% error levels. When the error levels were 16%, 24%, and 32%, the scores of musically experienced listeners were found to be significantly better than the scores of listeners with no musical experience. Also, in this same error level range, discrimination was significantly affected by the instrument tested. For error levels of 16% and 24%, discrimination scores were significantly, but negatively correlated with measures of spectral incoherence and normalized centroid deviation on unaltered instrument spectra, suggesting that the presence of dynamic spectral variations tends to increase the difficulty of detecting spectral alterations. Correlation between discrimination and a measure of spectral irregularity was comparatively low.

Musical Instrument Spectral Envelope Sensitivity

“…The first was a measurement of sensitivity which analyzed the individual Band Signal-to-Noise Ratios (BSNR). Following that, we computed two different distortion measures as employed in [4] and [5] to compare the data to previous studies. The final analysis compared the data to a simple psychoacoustic masking model as used in MPEG systems.…”

“…Horner [4] extended this work by observing instrument discrimination for random alterations to time-varying instrument spectra. He observed that discrimination was very good for 32% and 48% error levels, moderate for the 16% and 24% error levels and poor for the 8% error levels.…”

The Effects of MP3 Compression on Perceived Emotional Characteristics in Musical Instruments

“…The sustained instruments are nearly harmonic, and the chosen sounds had fundamental frequencies close to Eb4 (311.1 Hz). All eight instrument sounds were also used by a number of other timbre studies [40][41][42][43][44][45][46][47][48]. Using the same samples makes it easier to compare results.…”

“…This method was chosen for simplicity of comparison, since subjects only needed to remember two sounds for each comparison and make a binary decision. This required minimal memory from the subjects and allowed them to give more instantaneous responses [24,43]. Each combination of two different compressions was presented for each instrument and emotional category, and the listening test totaled P 4 2 × 8 × 10 = 960 trials.…”

Preparation of stimuli for timbre perception studies

“…In McAdams et al (1995) and Krimphoff et al (1994), one dimension also corresponds to the attack (characterized by the LRT). The influence of local spectrum variation or the shape of the spectral envelope has also been recognized as a salient attribute (Gabrielsson and Sj€ ogren, 1971;Grey and Gordon, 1978;Gunawan and Sen, 2008;Horner et al, 2004, Marozeau et al, 2003.…”