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spectro-temporal modulation framework  (SPECTRO Analytical)

 
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    SPECTRO Analytical spectro-temporal modulation framework
    A Song vs. Speech contrast (two-tailed) in the <t>STM</t> domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal <t>modulation</t> domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).
    Spectro Temporal Modulation Framework, supplied by SPECTRO Analytical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/spectro-temporal+modulation+framework/pmc11156671-42-11-11?v=SPECTRO+Analytical
    Average 90 stars, based on 1 article reviews
    spectro-temporal modulation framework - by Bioz Stars, 2026-08
    90/100 stars

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    1) Product Images from "Spectro-temporal acoustical markers differentiate speech from song across cultures"

    Article Title: Spectro-temporal acoustical markers differentiate speech from song across cultures

    Journal: Nature Communications

    doi: 10.1038/s41467-024-49040-3

    A Song vs. Speech contrast (two-tailed) in the STM domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal modulation domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).
    Figure Legend Snippet: A Song vs. Speech contrast (two-tailed) in the STM domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal modulation domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).

    Techniques Used: Two Tailed Test, Plasmid Preparation

    A R values in the STM domain for the correlation between fitted responses (PLS) and observed responses (STM) (FDR corrected in the spectral and temporal modulation domains, p < 0.05, two-tailed). Dark lines illustrate the boundaries of the significant effects presented in (Fig. ). B , C Left Panels: Scatter plot of fitted responses (PLS) and observed responses (STM) for the spectral (see letter (S) in ( A ), statistical peak ( B ), and temporal (see letter (T) in ( A ) statistical peak ( C )). Circles represents each speakers/vocalization ( n = 369), two-tailed, all ps < 0.001. Right panels: VIP scores: the horizontal bars show the acoustic features with the largest influence in the PLS. D Fieldsite-wise cross-validated ( n = 21 independent societies) support vector machine decoding accuracy (chance level: 50%) for four alternative strategies with STM features only (white), STM + acoustical features (black), Acoustical features only (gray) and Acoustical features without VIP variables (light gray). The colored dots (jet colormap sorted as a function of accuracy for the model trained with STM features only) represent the accuracy for each society. ** p < 0.001, ns non-significant, post hoc pairwise comparisons were two-tailed, Bonferroni corrected.
    Figure Legend Snippet: A R values in the STM domain for the correlation between fitted responses (PLS) and observed responses (STM) (FDR corrected in the spectral and temporal modulation domains, p < 0.05, two-tailed). Dark lines illustrate the boundaries of the significant effects presented in (Fig. ). B , C Left Panels: Scatter plot of fitted responses (PLS) and observed responses (STM) for the spectral (see letter (S) in ( A ), statistical peak ( B ), and temporal (see letter (T) in ( A ) statistical peak ( C )). Circles represents each speakers/vocalization ( n = 369), two-tailed, all ps < 0.001. Right panels: VIP scores: the horizontal bars show the acoustic features with the largest influence in the PLS. D Fieldsite-wise cross-validated ( n = 21 independent societies) support vector machine decoding accuracy (chance level: 50%) for four alternative strategies with STM features only (white), STM + acoustical features (black), Acoustical features only (gray) and Acoustical features without VIP variables (light gray). The colored dots (jet colormap sorted as a function of accuracy for the model trained with STM features only) represent the accuracy for each society. ** p < 0.001, ns non-significant, post hoc pairwise comparisons were two-tailed, Bonferroni corrected.

    Techniques Used: Two Tailed Test, Plasmid Preparation



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    spectro-temporal modulation framework  (SPECTRO Analytical)
    90
    SPECTRO Analytical spectro-temporal modulation framework
    A Song vs. Speech contrast (two-tailed) in the <t>STM</t> domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal <t>modulation</t> domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).
    Spectro Temporal Modulation Framework, supplied by SPECTRO Analytical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/spectro-temporal+modulation+framework/pmc11156671-42-11-11?v=SPECTRO+Analytical
    Average 90 stars, based on 1 article reviews
    spectro-temporal modulation framework - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    A Song vs. Speech contrast (two-tailed) in the STM domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal modulation domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).

    Journal: Nature Communications

    Article Title: Spectro-temporal acoustical markers differentiate speech from song across cultures

    doi: 10.1038/s41467-024-49040-3

    Figure Lengend Snippet: A Song vs. Speech contrast (two-tailed) in the STM domain across all societies ( p < 0.001, FDR corrected in the spectral and temporal modulation domains, n = 369 independent vocalizations). B Heatmap (smoothed) depicting the number of societies showing a significant effect in the clusters identified in ( A ). Each value reports a numeric count, with larger counts associated with black coloring. C K-means clustering of statistical peaks; dots represent each society. Dark lines illustrate the boundaries of the significant effects presented in ( A ). D Fieldsite-wise cross-validated support vector machine decoding accuracy (chance level: 50%). The colored dots represent the accuracy for each society (sorted as a function of accuracy with a jet colormap) n = 21 independent societies. E Receiver operating characteristic curve (ROC) for each society (same color code as in ( A ). Black dashed line represents the chance level . F Normalized feature weights in the modulation power spectrum domain showing features with the largest influence (z-score, average of the 21 classifiers) for the classifier. Dark lines illustrate the boundaries of the significant effects presented in ( A ).

    Article Snippet: We decomposed the acoustical signal of the vocalization samples using the Spectro-Temporal Modulation (STM) framework (Fig. ).

    Techniques: Two Tailed Test, Plasmid Preparation

    A R values in the STM domain for the correlation between fitted responses (PLS) and observed responses (STM) (FDR corrected in the spectral and temporal modulation domains, p < 0.05, two-tailed). Dark lines illustrate the boundaries of the significant effects presented in (Fig. ). B , C Left Panels: Scatter plot of fitted responses (PLS) and observed responses (STM) for the spectral (see letter (S) in ( A ), statistical peak ( B ), and temporal (see letter (T) in ( A ) statistical peak ( C )). Circles represents each speakers/vocalization ( n = 369), two-tailed, all ps < 0.001. Right panels: VIP scores: the horizontal bars show the acoustic features with the largest influence in the PLS. D Fieldsite-wise cross-validated ( n = 21 independent societies) support vector machine decoding accuracy (chance level: 50%) for four alternative strategies with STM features only (white), STM + acoustical features (black), Acoustical features only (gray) and Acoustical features without VIP variables (light gray). The colored dots (jet colormap sorted as a function of accuracy for the model trained with STM features only) represent the accuracy for each society. ** p < 0.001, ns non-significant, post hoc pairwise comparisons were two-tailed, Bonferroni corrected.

    Journal: Nature Communications

    Article Title: Spectro-temporal acoustical markers differentiate speech from song across cultures

    doi: 10.1038/s41467-024-49040-3

    Figure Lengend Snippet: A R values in the STM domain for the correlation between fitted responses (PLS) and observed responses (STM) (FDR corrected in the spectral and temporal modulation domains, p < 0.05, two-tailed). Dark lines illustrate the boundaries of the significant effects presented in (Fig. ). B , C Left Panels: Scatter plot of fitted responses (PLS) and observed responses (STM) for the spectral (see letter (S) in ( A ), statistical peak ( B ), and temporal (see letter (T) in ( A ) statistical peak ( C )). Circles represents each speakers/vocalization ( n = 369), two-tailed, all ps < 0.001. Right panels: VIP scores: the horizontal bars show the acoustic features with the largest influence in the PLS. D Fieldsite-wise cross-validated ( n = 21 independent societies) support vector machine decoding accuracy (chance level: 50%) for four alternative strategies with STM features only (white), STM + acoustical features (black), Acoustical features only (gray) and Acoustical features without VIP variables (light gray). The colored dots (jet colormap sorted as a function of accuracy for the model trained with STM features only) represent the accuracy for each society. ** p < 0.001, ns non-significant, post hoc pairwise comparisons were two-tailed, Bonferroni corrected.

    Article Snippet: We decomposed the acoustical signal of the vocalization samples using the Spectro-Temporal Modulation (STM) framework (Fig. ).

    Techniques: Two Tailed Test, Plasmid Preparation