Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel-based “mouse pup syllable classification calculator”

Abstract

Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified 10 syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

A Mutation Associated with Stuttering Alters Mouse Pup Ultrasonic Vocalizations

“…We chose this scheme because it was initially introduced based on quantitative observations of clustering, which makes it easy to automate and therefore unbiased. Several other syllable classification schemes have been used [11, 12, 18, 23, 26]; these alternative schemes differ primarily in the classification of syllables lacking pitch jumps, but they also exhibit many points of commonality. Wild type Gnptab wt/wt and knock-in Gnptab mut/mut mice were indistinguishable in the syllable types and their usage (see Experimental Procedures, p>.39, Figure S4).…”

Discrimination of partial from whole ultrasonic vocalizations using a go/no-go task in mice

“…Mice produce a wide variety of ultrasonic vocalizations (USVs), which have recently received increasing attention. Several researchers have attempted to classify these USVs; however, the types and numbers of categories differ greatly between studies (Portfors, 2007;Grimsley et al, 2011;Grimsley et al, 2012;Kikusui et al, 2011;Mahrt et al, 2013). So it remains unclear how USVs are processed by mice, although there is growing evidence that these USVs have biological relevance.…”

MUPET—Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations

“…To address the social meaning of USVs, the field has used syllable classification schemes, among the most popular being manual or semi-automated classification of syllables into ~9–12 broad categories based on spectral shapes (e.g., chevron, upward, frequency-step) (Grimsley et al, 2011; Portfors, 2007; Scattoni et al, 2008; Scattoni et al, 2010), or classification into ~4–15 categories based on instantaneous changes in frequency within a syllable (‘frequency jumps’) (Arriaga and Jarvis, 2013; Arriaga et al, 2012; Chabout et al, 2015; Holy and Guo, 2005; Mahrt et al, 2013). Categorization based on cluster analyses has revealed potentially meaningful spectral features in different strains and experimental contexts (Grimsley et al, 2013; Hammerschmidt et al, 2012; Sugimoto et al, 2011; von Merten et al, 2014). Several automated and semi-automated software programs have been developed to rapidly measure USV features and apply specific syllable classification schemes [e.g., SASLab Pro (Avisoft Bioacoustics, Germany), Mouse Song Analyzer v1.3 (MSA; (Arriaga et al, 2012; Chabout et al, 2015)], VoICE (Burkett et al, 2015)).…”

“…Several automated and semi-automated software programs have been developed to rapidly measure USV features and apply specific syllable classification schemes [e.g., SASLab Pro (Avisoft Bioacoustics, Germany), Mouse Song Analyzer v1.3 (MSA; (Arriaga et al, 2012; Chabout et al, 2015)], VoICE (Burkett et al, 2015)). Yet, there remains a lack of consensus on which classification schemes provide the best biological insights (Arriaga and Jarvis, 2013; Grimsley et al, 2013; von Merten et al, 2014), and indeed, the most informative spectro-temporal features may vary across genetic and environmental conditions. In our initial attempts to examine genetic and environmental factors that influence USV production and syllable repertoires in a large genetic reference panel (GRP) of recombinant inbred (RI) mouse strains, we encountered theoretical and technical challenges using current categorical approaches in large datasets (>500,000 syllables).…”