Auditory Selectivity for Spectral Contrast in Cortical Neurons and Behavior

Abstract

Vocal communication relies on the ability of listeners to identify, process, and respond to vocal sounds produced by others in complex environments. To accurately recognize these signals, animals' auditory systems must robustly represent acoustic features that distinguish vocal sounds from other environmental sounds. Vocalizations typically have spectral structure; power regularly fluctuates along the frequency axis, creating spectral contrast. Spectral contrast is closely related to harmonicity, which refers to spectral power peaks occurring at integer multiples of a fundamental frequency. Although both spectral contrast and harmonicity typify natural sounds, they may differ in salience for communication behavior and engage distinct neural mechanisms. Therefore, it is important to understand which of these properties of vocal sounds underlie the neural processing and perception of vocalizations. Here, we test the importance of vocalization-typical spectral features in behavioral recognition and neural processing of vocal sounds, using male zebra finches. We show that behavioral responses to natural and synthesized vocalizations rely on the presence of discrete frequency components, but not on harmonic ratios between frequencies. We identify a specific population of neurons in primary auditory cortex that are sensitive to the spectral resolution of vocal sounds. We find that behavioral and neural response selectivity is explained by sensitivity to spectral contrast rather than harmonicity. This selectivity emerges within the cortex; it is absent in the thalamorecipient region and present in the deep output region. Further, deep-region neurons that are contrast-sensitive show distinct temporal responses and selectivity for modulation density compared with unselective neurons.

Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny

“…Novel findings are that: (b1) syllables can be accurately classified by species using six acoustic features; and (b2) simple frequency features map onto phylogeny and have phylogenetic inertia, suggesting that changes in these features are constrained by phylogeny. Previous neurophysiological and behavioral studies suggest that songbirds are sensitive to complex acoustics and manipulations in the relationship between acoustic features 31 , 55 , 58 . Generalization of findings to different species, however, has been criticized given the limited number of species and acoustic features typically studied, and the limited consideration of interactions between acoustic features in previous research 59 .…”

“…Further, we predicted that species would show less differentiation in spectrotemporal features due to the diversity in spectrotemporal modulation across syllables and species 53 , 54 . We also predicted that features known to be used by birds for sound discrimination in behavioral studies (e.g., fundamental frequency, Weiner entropy, time entropy, frequency modulation) 31 , 55 – 57 would be the most important in syllable classification. Additionally, we predicted that song syllable classification accuracy would scale with acoustic similarity between species, such that greater overlap in acoustic feature space would predict greater misclassification errors in syllable labelling.…”

Genetically identified neurons in avian auditory pallium mirror core principles of their mammalian counterparts

“…[24][25][26][61][62][63][64][65][66] In avian primary auditory pallium, cell types identified by waveform and coding principles correspond to canonical cell types in mammalian primary auditory neocortex. 7,[67][68][69] Even so, the genetic identity of physiological cell types in the avian pallium has not been resolved.…”

Utility indicator for emotion detection in a speaker authentication system

“…A variety of techniques can be used to extract features. For example: Mel frequency cepstral coefficient (MFCC) (Cho et al , 2019), linear predictive coding (Thullier et al , 2017), Mel scale cepstral analysis (MEL) (Hyder et al , 2017), discrete wavelet transform (Hammami et al , 2020), zero-crossing rate (Özaydın, 2019), Tonnetz (Alkhawaldeh, 2019), chroma (Garg et al , 2020) or spectral contrast (contrast) (So et al , 2020). This paper is not to select the best feature extraction technique but proposes and demonstrates an approach to compare techniques in the context of identifying duress, which may consist of multiple emotions.…”