J. Krimphoff

Bio: J. Krimphoff is an academic researcher from IRCAM. The author has contributed to research in topics: Spectral centroid & Timbre. The author has an hindex of 1, co-authored 1 publications receiving 573 citations.

Perceptual scaling of synthesized musical timbres: Common dimensions, specificities, and latent subject classes

Abstract: To study the perceptual structure of musical timbre and the effects of musical training, timbral dissimilarities of synthesized instrument sounds were rated by professional musicians, amateur musicians, and nonmusicians The data were analyzed with an extended version of the multidimensional scaling algorithm CLASCAL (Winsberg & De Soete, 1993), which estimates the number of latent classes of subjects, the coordinates of each timbre on common Euclidean dimensions, a specificity value of unique attributes for each timbre, and a separate weight for each latent class on each of the common dimensions and the set of specificities Five latent classes were found for a three-dimensional spatial model with specificities Common dimensions were quantified psychophysically in terms of log-rise time, spectral centroid, and degree of spectral variation The results further suggest that musical timbres possess specific attributes not accounted for by these shared perceptual dimensions Weight patterns indicate that perceptual salience of dimensions and specificities varied across classes A comparison of class structure with biographical factors associated with degree of musical training and activity was not clearly related to the class structure, though musicians gave more precise and coherent judgments than did nonmusicians or amateurs The model with latent classes and specificities gave a better fit to the data and made the acoustic correlates of the common dimensions more interpretable

Sensitivity to musical emotion is influenced by tonal structure in congenital amusia

Abstract: Emotional communication in music depends on multiple attributes including psychoacoustic features and tonal system information, the latter of which is unique to music. The present study investigated whether congenital amusia, a lifelong disorder of musical processing, impacts sensitivity to musical emotion elicited by timbre and tonal system information. Twenty-six amusics and 26 matched controls made tension judgments on Western (familiar) and Indian (unfamiliar) melodies played on piano and sitar. Like controls, amusics used timbre cues to judge musical tension in Western and Indian melodies. While controls assigned significantly lower tension ratings to Western melodies compared to Indian melodies, thus showing a tonal familiarity effect on tension ratings, amusics provided comparable tension ratings for Western and Indian melodies on both timbres. Furthermore, amusics rated Western melodies as more tense compared to controls, as they relied less on tonality cues than controls in rating tension for Western melodies. The implications of these findings in terms of emotional responses to music are discussed.

The Timbre Toolbox: extracting audio descriptors from musical signals.

Abstract: The analysis of musical signals to extract audio descriptors that can potentially characterize their timbre has been disparate and often too focused on a particular small set of sounds. The Timbre Toolbox provides a comprehensive set of descriptors that can be useful in perceptual research, as well as in music information retrieval and machine-learning approaches to content-based retrieval in large sound databases. Sound events are first analyzed in terms of various input representations (short-term Fourier transform, harmonic sinusoidal components, an auditory model based on the equivalent rectangular bandwidth concept, the energy envelope). A large number of audio descriptors are then derived from each of these representations to capture temporal, spectral, spectrotemporal, and energetic properties of the sound events. Some descriptors are global, providing a single value for the whole sound event, whereas others are time-varying. Robust descriptive statistics are used to characterize the time-varying descriptors. To examine the information redundancy across audio descriptors, correlational analysis followed by hierarchical clustering is performed. This analysis suggests ten classes of relatively independent audio descriptors, showing that the Timbre Toolbox is a multidimensional instrument for the measurement of the acoustical structure of complex sound signals.

Musical instrument recognition using cepstral coefficients and temporal features

Abstract: In this paper, a system for pitch independent musical instrument recognition is presented. A wide set of features covering both spectral and temporal properties of sounds was investigated, and their extraction algorithms were designed. The usefulness of the features was validated using test data that consisted of 1498 samples covering the full pitch ranges of 30 orchestral instruments from the string, brass and woodwind families, played with different techniques. The correct instrument family was recognized with 94% accuracy and individual instruments in 80% of cases. These results are compared to those reported in other work. Also, utilization of a hierarchical classification framework is considered.