Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations

O_LIBirdsong is an important signal in mate attraction and territorial defense. Quantifying the complexity of these songs can shed light on individual fitness, sexual selection, and behavior. Several techniques have been used to quantify song complexity and be broadly categorized into diversity indices, measures of stationary probabilities, and measures of sequential variations. However, these methods are unable to account for important acoustic features like the frequency bandwidth and the variety in the shape of syllables which are an integral part of these vocal signals. This study proposes a new complexity measure that considers intra-song note variability and calculates a weighted index for birdsongs using spectral cross-correlation. C_LIO_LIWe compared the previously described methods to understand the advantages and limitations based on the factors that would be affecting the complexity of songs. We developed a new method- Note Variability Index (NVI), which incorporates the spectral features of notes while quantifying complexity. This measure alleviates the need for manual annotations of notes that can be error-prone. We used Spectrogram Cross-Correlation (SPCC) to compare notes within a song and used the output values to quantify song complexity. C_LIO_LITo check for the efficacy of the new method, we generated synthetic songs to caricature extremes in song complexity and compared selected conventional complexity measures along with the NVI. We provide case-specific limitations of these methods. Additionally, to examine the efficacy of this new method in real-world scenarios, we used natural birdsongs from multiple species across the globe with varying song structures to compare conventional methods with NVI. C_LIO_LITo our knowledge, NVI is the only song complexity method that captures the variation of spectral features of notes in songs where the conventional methods fail to distinguish between similar song structures with different note types. As NVI does not need a manual classification of notes, it can be easily implemented for any type of birdsong with existing sound analysis softwares; it is very quick, avoids the possible biases in note classification, and can possibly be automated for large datasets in the future. C_LI