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VoICE: A semi-automated pipeline for standardizing vocal analysis across models.

Burkett ZD, Day NF, Peñagarikano O, Geschwind DH, White SA - Sci Rep (2015)

Bottom Line: When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing.In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2.VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Integrative Biology &Physiology, University of California, Los Angeles, California 90095 [2] Interdepartmental Program in Molecular, Cellular, &Integrative Physiology, University of California, Los Angeles, California 90095.

ABSTRACT
The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization "types" by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.

No MeSH data available.


Related in: MedlinePlus

Assignment and quantification of clustered birdsong syllables.(a) Mature zebra finches (>120d) sing stereotyped song composed of repeated syllables that form motifs that form bouts. Shown are two song bouts sung by the same adult bird during two recording epochs (‘Session A’ and ‘Session B’). (Scale bar = 250 msec.) (b) Dendrogram plots global similarity distance between leaves (syllables) and was generated following spectral similarity scoring. Beneath the branches, clusters before (Unmerged) and after merging (Merged) are denoted by color bands. Representative syllables from merged clusters are illustrated at descending percentiles following correlation of each cluster member to the cluster eigensyllable. The Pearson’s rho for the correlation between each syllable and its eigensyllable are displayed in white. (c) During assignment, one of three possible outcomes for each syllable occurs: automatic assignment to a cluster (ASSIGNMENT), manual assignment in a tiebreaking procedure when statistically similar to two clusters (TIE), or categorization as novel (NOVEL). Artificially introduced syllables from a Bengalese finch did not pass a global similarity floor and are accurately deemed ‘novel’. Bars indicate the mean percentage global similarity between the syllable and each cluster. (d) The two artificially introduced syllables from a Bengalese finch, are, upon merging (Merged), appropriately assigned to two novel clusters. (e) Syntaxes are highly similar between recording sessions, regardless of metric used for scoring (left, ‘unmodified’) but the artificial introduction of novel syllables to the second recording session reduces similarity when using a metric that penalizes for novel syllables (right, ‘modified’). (f) Pitch (top) and entropy (bottom) are largely unchanged between recording sessions. (* = p < 0.05, resampling independent mean differences. Cluster colors are consistent throughout. Scale bars = 50 msec.)
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f1: Assignment and quantification of clustered birdsong syllables.(a) Mature zebra finches (>120d) sing stereotyped song composed of repeated syllables that form motifs that form bouts. Shown are two song bouts sung by the same adult bird during two recording epochs (‘Session A’ and ‘Session B’). (Scale bar = 250 msec.) (b) Dendrogram plots global similarity distance between leaves (syllables) and was generated following spectral similarity scoring. Beneath the branches, clusters before (Unmerged) and after merging (Merged) are denoted by color bands. Representative syllables from merged clusters are illustrated at descending percentiles following correlation of each cluster member to the cluster eigensyllable. The Pearson’s rho for the correlation between each syllable and its eigensyllable are displayed in white. (c) During assignment, one of three possible outcomes for each syllable occurs: automatic assignment to a cluster (ASSIGNMENT), manual assignment in a tiebreaking procedure when statistically similar to two clusters (TIE), or categorization as novel (NOVEL). Artificially introduced syllables from a Bengalese finch did not pass a global similarity floor and are accurately deemed ‘novel’. Bars indicate the mean percentage global similarity between the syllable and each cluster. (d) The two artificially introduced syllables from a Bengalese finch, are, upon merging (Merged), appropriately assigned to two novel clusters. (e) Syntaxes are highly similar between recording sessions, regardless of metric used for scoring (left, ‘unmodified’) but the artificial introduction of novel syllables to the second recording session reduces similarity when using a metric that penalizes for novel syllables (right, ‘modified’). (f) Pitch (top) and entropy (bottom) are largely unchanged between recording sessions. (* = p < 0.05, resampling independent mean differences. Cluster colors are consistent throughout. Scale bars = 50 msec.)

Mentions: Zebra finch songs consist of multiple syllables that are repeated in a specific pattern to form motifs, the neuroethologically relevant unit of a song16 (Fig. 1a). To validate VoICE in birdsong analysis, we examined the first ~300 syllables sung on two separate days, seven days apart. ‘Session A’ comprised 308 syllables and ‘Session B’ comprised 310. Due to the stereotyped nature of adult song, we predicted that songs would retain their phonology and syntax over time; an outcome that would support the utility of VoICE. Syllables from the Session A were extracted using the “Explore and Score” module of Sound Analysis Pro8 (SAP). Similarity scores between all syllables were calculated (Fig. S1) and the resultant similarity matrix was imported and hierarchically clustered in R, resulting in the production of a dendrogram. The algorithm produced 54 unique clusters, which were merged to 8 final clusters by a guided procedure (Methods, Supplementary Note 1), each representing a syllable in the motif (Fig. 1b). For each cluster, an ‘eigensyllable’ was calculated to represent the syllable that best describes the variance within the cluster (Methods). The syllables in each cluster were correlated to the eigensyllable and ranked to determine overall homogeneity in the cluster. The syllable with the lowest correlation to the eigensyllable was visually inspected to ensure that all syllables were properly assigned to each cluster. The average correlation of the lowest ranked syllable to the eigensyllable across all clusters was 0.788, which captures the stereotypy of adult birdsong.


VoICE: A semi-automated pipeline for standardizing vocal analysis across models.

Burkett ZD, Day NF, Peñagarikano O, Geschwind DH, White SA - Sci Rep (2015)

Assignment and quantification of clustered birdsong syllables.(a) Mature zebra finches (>120d) sing stereotyped song composed of repeated syllables that form motifs that form bouts. Shown are two song bouts sung by the same adult bird during two recording epochs (‘Session A’ and ‘Session B’). (Scale bar = 250 msec.) (b) Dendrogram plots global similarity distance between leaves (syllables) and was generated following spectral similarity scoring. Beneath the branches, clusters before (Unmerged) and after merging (Merged) are denoted by color bands. Representative syllables from merged clusters are illustrated at descending percentiles following correlation of each cluster member to the cluster eigensyllable. The Pearson’s rho for the correlation between each syllable and its eigensyllable are displayed in white. (c) During assignment, one of three possible outcomes for each syllable occurs: automatic assignment to a cluster (ASSIGNMENT), manual assignment in a tiebreaking procedure when statistically similar to two clusters (TIE), or categorization as novel (NOVEL). Artificially introduced syllables from a Bengalese finch did not pass a global similarity floor and are accurately deemed ‘novel’. Bars indicate the mean percentage global similarity between the syllable and each cluster. (d) The two artificially introduced syllables from a Bengalese finch, are, upon merging (Merged), appropriately assigned to two novel clusters. (e) Syntaxes are highly similar between recording sessions, regardless of metric used for scoring (left, ‘unmodified’) but the artificial introduction of novel syllables to the second recording session reduces similarity when using a metric that penalizes for novel syllables (right, ‘modified’). (f) Pitch (top) and entropy (bottom) are largely unchanged between recording sessions. (* = p < 0.05, resampling independent mean differences. Cluster colors are consistent throughout. Scale bars = 50 msec.)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4446892&req=5

f1: Assignment and quantification of clustered birdsong syllables.(a) Mature zebra finches (>120d) sing stereotyped song composed of repeated syllables that form motifs that form bouts. Shown are two song bouts sung by the same adult bird during two recording epochs (‘Session A’ and ‘Session B’). (Scale bar = 250 msec.) (b) Dendrogram plots global similarity distance between leaves (syllables) and was generated following spectral similarity scoring. Beneath the branches, clusters before (Unmerged) and after merging (Merged) are denoted by color bands. Representative syllables from merged clusters are illustrated at descending percentiles following correlation of each cluster member to the cluster eigensyllable. The Pearson’s rho for the correlation between each syllable and its eigensyllable are displayed in white. (c) During assignment, one of three possible outcomes for each syllable occurs: automatic assignment to a cluster (ASSIGNMENT), manual assignment in a tiebreaking procedure when statistically similar to two clusters (TIE), or categorization as novel (NOVEL). Artificially introduced syllables from a Bengalese finch did not pass a global similarity floor and are accurately deemed ‘novel’. Bars indicate the mean percentage global similarity between the syllable and each cluster. (d) The two artificially introduced syllables from a Bengalese finch, are, upon merging (Merged), appropriately assigned to two novel clusters. (e) Syntaxes are highly similar between recording sessions, regardless of metric used for scoring (left, ‘unmodified’) but the artificial introduction of novel syllables to the second recording session reduces similarity when using a metric that penalizes for novel syllables (right, ‘modified’). (f) Pitch (top) and entropy (bottom) are largely unchanged between recording sessions. (* = p < 0.05, resampling independent mean differences. Cluster colors are consistent throughout. Scale bars = 50 msec.)
Mentions: Zebra finch songs consist of multiple syllables that are repeated in a specific pattern to form motifs, the neuroethologically relevant unit of a song16 (Fig. 1a). To validate VoICE in birdsong analysis, we examined the first ~300 syllables sung on two separate days, seven days apart. ‘Session A’ comprised 308 syllables and ‘Session B’ comprised 310. Due to the stereotyped nature of adult song, we predicted that songs would retain their phonology and syntax over time; an outcome that would support the utility of VoICE. Syllables from the Session A were extracted using the “Explore and Score” module of Sound Analysis Pro8 (SAP). Similarity scores between all syllables were calculated (Fig. S1) and the resultant similarity matrix was imported and hierarchically clustered in R, resulting in the production of a dendrogram. The algorithm produced 54 unique clusters, which were merged to 8 final clusters by a guided procedure (Methods, Supplementary Note 1), each representing a syllable in the motif (Fig. 1b). For each cluster, an ‘eigensyllable’ was calculated to represent the syllable that best describes the variance within the cluster (Methods). The syllables in each cluster were correlated to the eigensyllable and ranked to determine overall homogeneity in the cluster. The syllable with the lowest correlation to the eigensyllable was visually inspected to ensure that all syllables were properly assigned to each cluster. The average correlation of the lowest ranked syllable to the eigensyllable across all clusters was 0.788, which captures the stereotypy of adult birdsong.

Bottom Line: When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing.In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2.VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Integrative Biology &Physiology, University of California, Los Angeles, California 90095 [2] Interdepartmental Program in Molecular, Cellular, &Integrative Physiology, University of California, Los Angeles, California 90095.

ABSTRACT
The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization "types" by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.

No MeSH data available.


Related in: MedlinePlus