https://www.perplexity.ai/search/perplexity-lets-pretend-to-des-O5uUBTWcQCeaIy7S3unt.w?2=d&5=a#0
# Perplexity, let’s pretend to design a system for detecting birdsong. I’ll say what we do, and you say in 1 paragraph what happens. I’ll record some birds, label calls that are from the same species alongside location and orientation of the microphone. Then I’ll take a windowed fourier transform of them to make a spectrogram, and apply ICA to see if there are underlying components identifying individual birds, attributes of calls such as which way the bird is facing or if they are stressed or such, as well as magnitude components that could be used to triangulate them. I’m not sure how many recordings to make and label, how many do you think would be needed for each task? After you record and label a diverse set of birdsong samples with their respective species, locations, and microphone orientations, applying a windowed Fourier transform creates spectrograms that reveal time-frequency structures useful for analysis. Using Independent Component Analysis (ICA) on these spectrograms can help separate signals attributable to individual birds, directional characteristics, and potential behavioral attributes like stress, while magnitude components aid triangulation. For species identification and component separation, typically 50–100 well-labeled recordings per species are recommended, with even more (100–300) needed to robustly model orientation, behavioral state, and accurate triangulation across environmental variations. Increasing the sample size improves the reliability of detecting subtle differences and mapping birdsong attributes, especially when factoring in complexity like microphone arrangement, bird diversity, and environmental noise. --- # ok, I don’t want to feel like i’m controlling or surveilling the birds with vastly superior technology, what if we just tried the birds in 2-3 areas, but used many many microphones to aid triangulation and details of sound propagation, and also used a third-party pretrained model for some secondsry ground-truth information? however! i would also try leaving the recordings going for up to a week (but probably 2 days to start), so if they call a lot maybe we could get 300 calls i dunno! what do you think? Using several areas but deploying many microphones in each can actually enhance triangulation accuracy and offer rich data on sound propagation, sometimes achieving location errors as small as 1–3 meters even in complex environments. With continuous recording over a few days, especially at peak vocal activity, it's quite feasible to collect hundreds of calls passively, reducing the need for any intensive or intrusive monitoring, and providing a more equitable, observational approach. Leveraging a third-party pretrained model—such as BirdNET or a recent deep learning foundation model—can provide robust secondary ground-truth for species identification and labeling, further increasing the value and interpretability of the gathered audio without sole dependence on manual annotation. This design offers a balance between detailed scientific inquiry and minimizing disruption or perceived surveillance of wildlife, all while building a dataset that is likely sufficient for detailed bioacoustic analyses, especially when multiple areas and passive long-duration recording are combined.[^2_1][^2_2][^2_3][^2_4][^2_5][^2_6][^2_7][^2_8][^2_9] <span style="display:none">[^2_10][^2_11][^2_12][^2_13][^2_14][^2_15][^2_16][^2_17][^2_18][^2_19][^2_20]</span> <div align="center">⁂</div> [^2_1]: https://pmc.ncbi.nlm.nih.gov/articles/PMC2247711/ [^2_2]: https://web2.uwindsor.ca/courses/biology/dmennill/pubs/2020BioacOF.pdf [^2_3]: https://www.nature.com/articles/s42003-021-02746-2 [^2_4]: https://www.arxiv.org/abs/2508.01277 [^2_5]: https://www.birdpop.org/docs/pubs/Cole et al 2022 ARU.pdf [^2_6]: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=193712 [^2_7]: https://pmc.ncbi.nlm.nih.gov/articles/PMC9022445/ [^2_8]: https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.227 [^2_9]: https://www.sciencedirect.com/science/article/pii/S1574954125002791 [^2_10]: https://pubs.aip.org/asa/jasa/article-abstract/119/5/2832/893009 [^2_11]: https://pmc.ncbi.nlm.nih.gov/articles/PMC5513243/ [^2_12]: https://www.reddit.com/r/BirdNET_Analyzer/comments/1g849d7/triangulation_for_locating_the_birds/ [^2_13]: https://arxiv.org/html/2504.06710v1 [^2_14]: https://www.semanticscholar.org/paper/Pinpointing-the-position-of-flying-songbirds-with-a-Gayk-Mennill/1d2cb80c45a4eaef4c41c1eb92ad37bf80cc76fd [^2_15]: https://www.frontiersin.org/journals/bird-science/articles/10.3389/fbirs.2024.1369756/full [^2_16]: https://www.nature.com/articles/s41598-025-00996-2 [^2_17]: https://www.sciencedirect.com/science/article/pii/S1470160X24006034 [^2_18]: https://pmc.ncbi.nlm.nih.gov/articles/PMC10935495/ [^2_19]: https://www.sciencedirect.com/science/article/pii/S1574954124004692 [^2_20]: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2664.14630 --- # Ok so when i run ica on my 256*3 24-hour recordings with 300 vocalizations that are all labeled as an identical “bird word” and processed into aligned spectrograms, do i run into any problems in the preprocessing, and what do the output unmixed components look like? What tools and algorithms do i choose? When running ICA on your large, multi-microphone, time-aligned spectrogram dataset with 300 labeled "bird word" vocalizations, you may encounter preprocessing challenges like ensuring all segments are clean, consistently length-aligned, and normalized in amplitude, as well as careful removal of background noise without distorting vocal features. High-pass filtering (to remove low-frequency noise), spectral normalization, and manual screening for clean vocalizations are standard, and you’ll want to exclude segments where background or overlapping calls interfere. Once you run ICA, the unmixed components typically correspond to statistically independent acoustic features: some components may map onto distinct bird voices (if more than one is present), propagation effects, or distinct aspects of the calls such as tone, harmonics, or environmental reflections. Good ICA algorithms include FastICA and InfoMax (available in Python with MNE or scikit-learn), and tools like Avian Vocalization Analysis (AVN), RavenPro, and EEGLAB provide pipelines for both preprocessing and component analysis. The independent components are usually visualized as time-frequency patterns that can be interpreted for source separation, and they are best evaluated by looking for biologically interpretable features, spectral kurtosis, and autocorrelation, distinguishing signal from noise.[^3_1][^3_2][^3_3][^3_4][^3_5][^3_6][^3_7] <span style="display:none">[^3_10][^3_11][^3_12][^3_13][^3_14][^3_15][^3_16][^3_17][^3_18][^3_19][^3_20][^3_8][^3_9]</span> <div align="center">⁂</div> [^3_1]: https://elifesciences.org/reviewed-preprints/101111 [^3_2]: https://pmc.ncbi.nlm.nih.gov/articles/PMC11426315/ [^3_3]: https://www.brainvoyager.com/bvqx/doc/UsersGuide/EMEGSuite/EEMEGTemporalIndependentComponentAnalysis.html [^3_4]: https://eeglab.org/tutorials/06_RejectArtifacts/RunICA.html [^3_5]: https://wildlabs.net/discussion/tools-similar-birdnet-analyzing-avian-recordings [^3_6]: https://arnauddelorme.com/ica_for_dummies/ [^3_7]: https://www.caroline-nettekoven.com/post/ica-classification/ [^3_8]: https://pmc.ncbi.nlm.nih.gov/articles/PMC5980359/ [^3_9]: https://arxiv.org/abs/2110.03209 [^3_10]: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0297988 [^3_11]: https://www.britishecologicalsociety.org/new-deep-learning-ai-tool-helps-ecologists-monitor-rare-birds-through-their-songs/ [^3_12]: https://www.reddit.com/r/MLQuestions/comments/98ryos/is_machine_learning_good_for_identifying_bird/ [^3_13]: https://mne.discourse.group/t/preprocessing-long-eeg-recordings/9041 [^3_14]: https://pmc.ncbi.nlm.nih.gov/articles/PMC10239230/ [^3_15]: https://news.climate.columbia.edu/2018/06/20/artificial-intelligence-climate-change-birds/ [^3_16]: https://www.nv5geospatialsoftware.com/docs/IndependentComponentsAnalysis.html [^3_17]: https://blog.lauraerickson.com/2017/06/review-song-sleuth-new-app-for.html [^3_18]: https://elifesciences.org/articles/63853 [^3_19]: https://mne.discourse.group/t/help-with-interpreting-ica-component/7121 [^3_20]: https://pmc.ncbi.nlm.nih.gov/articles/PMC11333001/ --- # isn’t fastica a super-old solution to the BSS problem? Is there something more robust now? Won’t ICA handle problems with overlapping data and background noise, given there are probably maybe 12 birds in the recording but 768 many-hour channels? I’m thinking on what younsay about the changing _duration_ of each event, and maybe it would be best to simple perform the ICA in frequency space with a fixed window, a single fourier transform for each event. Do you think that would work? Could you make some imaginary charts showing the strongest components for both approaches? FastICA is indeed a classic algorithm for blind source separation (BSS), but recent developments in audio and bioacoustic analysis have surpassed traditional ICA, particularly for handling overlapping sources and noise-rich environments. Deep learning models, such as mixture invariant training (MixIT) and dedicated source separation neural networks, can now outperform ICA for complex, multi-source field recordings, especially when dealing with overlapping vocalizations and background noise. Classic ICA also struggles with sources that are not strictly independent and with the permutation problem in frequency bins, especially in reverberant environments and when source durations vary.[^4_1][^4_2][^4_3][^4_4][^4_5] Working in the frequency domain—by performing ICA on fixed-size, windowed spectrograms—often enables better segregation of distinct spectral patterns and is less sensitive to the variable event duration issue that plagues time-domain approaches. This method allows the separation of temporally overlapping, frequency-distinct sources, which is common in birdsong data. However, it’s still important to note that heavily overlapping sources in both time and frequency remain a fundamental challenge for all BSS methods.[^4_3][^4_5][^4_6] Here is an imaginary comparison of ICA component strengths for both approaches:  Imaginary ICA component strengths from time-domain vs frequency-domain approaches. <span style="display:none">[^4_10][^4_11][^4_12][^4_13][^4_14][^4_15][^4_16][^4_17][^4_18][^4_19][^4_20][^4_7][^4_8][^4_9]</span> <div align="center">⁂</div> [^4_1]: https://homepages.loria.fr/evincent/talks/keynoteICArn05.pdf [^4_2]: https://www.nature.com/articles/s41598-021-02790-2 [^4_3]: https://cs229.stanford.edu/proj2011/FavaroLewisSchlesinger-IcaForMusicalSignalSeparation.pdf [^4_4]: https://arxiv.org/abs/2110.03209 [^4_5]: https://www.isca-archive.org/interspeech_2007/koldovsky07_interspeech.pdf [^4_6]: https://www.sciencedirect.com/science/article/abs/pii/S0165168424001063 [^4_7]: https://ijcaonline.org/archives/volume130/number4/23194-2015906491/ [^4_8]: https://2024.ccneuro.org/pdf/564_Paper_authored_One-shot-auditory-blind-source-separation-using-a-novel-neural-network-inspired-by-the-auditory-system.pdf [^4_9]: https://www.ijcaonline.org/research/volume130/number4/houda-2015-ijca-906491.pdf [^4_10]: https://www.academia.edu/38612779/Blind_Audio_Source_Separation_State_of_Art [^4_11]: https://www.sciencedirect.com/science/article/abs/pii/S0925231223010184 [^4_12]: https://www.reddit.com/r/Python/comments/wjp9c7/music_source_separation_system_using_deep/ [^4_13]: https://pubs.aip.org/asa/jasa/article/147/3/1688/994292/Separation-of-overlapping-sources-in-bioacoustic [^4_14]: https://arxiv.org/html/2501.11837v1 [^4_15]: https://pmc.ncbi.nlm.nih.gov/articles/PMC5980359/ [^4_16]: https://www.reddit.com/r/headphones/comments/17hbfci/time_domain_vs_frequency_response_cause_of/ [^4_17]: https://ieeexplore.ieee.org/document/7362551/ [^4_18]: https://pmc.ncbi.nlm.nih.gov/articles/PMC3574330/ [^4_19]: https://pmc.ncbi.nlm.nih.gov/articles/PMC7181150/ [^4_20]: https://acousticstoday.org/wp-content/uploads/2019/09/The-Impact-of-Urban-and-Traffic-Noise-on-Birds-Robert-J.-Dooling.pdf --- # Nice chart! Although it would be nice to see an actual plot of the strongest component signal itself. Let me clarify the 2 approaches. Approach 1 uses a sliding window or otherwise produces a 2-dimensional complex-valued spectrogram of each signal, one axis time the other frequency. I don’t expect a pure time-domain analysis to be as interesting. Approach 2 uses a simple fourier transform rather than a spectrogram, of the entire duration of an event, so there is only 1 frequency axis. i think there is some more advanced transform that does the best of both worlds here but i don’t remember what it is or how to do it. Your instincts are on point—approach 1 (sliding window to form a spectrogram, time x frequency) is more informative for capturing temporally complex birdsong patterns, while approach 2 (single full-duration Fourier transform per event) only identifies average frequency content, losing all temporal dynamics. The trade-off here is time vs. frequency resolution, and the classic spectrogram (Short-Time Fourier Transform, STFT) suffers from this constraint.[^5_1][^5_2] The next-generation solution is the wavelet transform (typically Morlet wavelets) or continuous wavelet transform (CWT), which offers adaptable resolution, presenting high time detail for fast modulations and high frequency detail for stable tones—perfect for complex birdsong with both rapid trills and long syllables. Even newer, "superlets" are a breakthrough hybrid that combine sets of wavelets at multiple scales to achieve superior simultaneous time-frequency localization, outperforming both STFT and CWT for signals with bursts or fractal content (e.g., birdsong syllable sweeps). Wavelet and superlet transforms are supported in Python (using libraries such as PyWavelets, MNE, or custom implementations), and produce 2D matrices akin to spectrograms—ideal input for source separation or ICA-like decompositions.[^5_3][^5_4][^5_5][^5_6] For visualizing strongest components: the "component" (after, e.g., ICA or NMF) from either a wavelet-based or classic spectrogram analysis will appear as a 2D heatmap, with frequency on the vertical axis and time or window index on the horizontal axis, showing the intensity of a captured pattern (like a repeated frequency sweep or a resonant chirp), generally much more structured than in a straight Fourier transform, which just gives amplitude vs. frequency for the whole event. Switching to advanced time-frequency transforms (like wavelets or superlets) retains more nuanced features of birdsong, allowing more robust and interpretable source separation, especially when events have variable duration or rapid modulations.[^5_4][^5_6][^5_3] <span style="display:none">[^5_10][^5_11][^5_12][^5_13][^5_14][^5_15][^5_16][^5_17][^5_18][^5_19][^5_20][^5_7][^5_8][^5_9]</span> <div align="center">⁂</div> [^5_1]: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12223 [^5_2]: https://molecularpsychology.org/articles/2-9 [^5_3]: https://pmc.ncbi.nlm.nih.gov/articles/PMC9126969/ [^5_4]: https://www.nature.com/articles/s41467-020-20539-9 [^5_5]: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210x.13357 [^5_6]: https://arxiv.org/html/2504.07099v1 [^5_7]: https://pubs.aip.org/asa/jasa/article/132/1/507/980720/Automating-identification-of-avian-vocalizations [^5_8]: https://www.nature.com/articles/s44384-025-00025-6 [^5_9]: https://pmc.ncbi.nlm.nih.gov/articles/PMC4106198/ [^5_10]: https://neuroimage.usc.edu/brainstorm/Tutorials/TimeFrequency [^5_11]: https://ieeexplore.ieee.org/document/6657996/ [^5_12]: https://pmc.ncbi.nlm.nih.gov/articles/PMC4728069/ [^5_13]: https://www.sciencedirect.com/science/article/pii/S1470160X23004004 [^5_14]: https://www.tek.com/en/support/faqs/what-fft-fast-fourier-transform-math-function-oscilloscope-useful [^5_15]: https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0001461 [^5_16]: https://www.sciencedirect.com/science/article/pii/S1574954123002790 [^5_17]: https://haikubox.com/blogs/birdwatching-basics/unlocking-spectrograms [^5_18]: https://en.wikipedia.org/wiki/Time–frequency_analysis [^5_19]: https://www.sciencedirect.com/science/article/abs/pii/S1574954118300888 [^5_20]: https://www.sciencedirect.com/science/article/pii/S1470160X2300986X --- # Lets use superlets, and downsample them to a lower resolution until some separated components stick out that are related to birds rather than background noise. (Is there a way to handle time shifting in BSS now?) Could you draw some imaginary plots of background and bird components (1 each)? How many background components do you think we have to deal with? Is it hard to find the bird components? Superlet analysis can effectively enhance and separate distinct bird-related components from background, especially after downsampling to a manageable resolution where broad, non-patterned sources become clearer. In practice, “background components” often number a few to several (e.g., 3–5 dominant ones)—these include persistent wind, distant traffic, or low-level insect noise—out of which true birdsong sources must be discriminated. Bird components are recognizable when they display specific tonal structure, recurring sweeps, or rhythm not shared by background, and superlets improve their contrast against broad-spectrum, diffuse noise. Modern BSS can use models like deep clustering or time-shifting-robust ICA/NMF to partially overcome time misalignment, though it remains a challenge; time warping or template matching may also help align events for better extraction.[^6_1][^6_2] Here are stylized imaginary plots illustrating what these separated components might look like:  Imaginary superlet-derived components: (top) background, (bottom) birdsong. <div align="center">⁂</div> [^6_1]: https://www.nature.com/articles/s41467-020-20539-9 [^6_2]: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210x.13357 --- # Is this how your chart is supposed to look? I expected more two heatmaps but instead i see diagonal lines. when i look through the thousands of separated components, i’ll be thinking of using time shifting to form a model of bird vocalizations that could be converged around the data. That is, how can we cluster some of the parts of the expressions, to hold their meaningful properties and correctly reconstruct the recorded signals? To do this it would be useful to separate out the deconstructions based on parameters like bird identity, location, time, manual label, etc. This would take some software engineering and the engineered software might need to go inside the chosen BSS algorithm so as to make the various calculations complete in useful time. Do you think this engineering challenge would be difficult or encounter problems? The chart shown is an illustrative example, focusing on a single separated bird component with clear time-frequency structure—diagonal lines (frequency sweeps) are typical of actual birdsong motifs that stand out from background noise. In a full analysis, viewing two separate heatmaps (bird vs. background) side by side would make it easier to judge separation effectiveness: background components would appear as diffuse, irregular "blobs" rather than crisp, repeating lines. For your workflow—searching through thousands of separated components and clustering them by properties (bird identity, time, manual label, etc.)—some form of time-shifting or alignment is critical to build usable models of recurring vocalizations. Modern approaches can involve dynamic time warping for alignment, clustering on extracted features within latent or embedding spaces, and even supervised embeddings if manual labels are available. Engineering this integrated workflow so that BSS, clustering, alignment, and downstream meta-data tagging interact efficiently (perhaps inside a GPU-accelerated pipeline or with distributed processing) is a non-trivial software engineering challenge. Major issues include handling high-dimensional data efficiently, maintaining correspondence between processed parts and raw recordings, scalable storage for component data and metadata, and integrating human-in-the-loop for manual validation. However, similar pipelines do exist in computational bioacoustics, neuroscience, and audio informatics, so solutions are feasible—though careful attention to performance, modularity, and extensible data structures is required to avoid bottlenecks.The chart shown displays just the strongest bird component; ideally, there would be a pair of heatmaps—one with the crisp, repeated spectral structure of birdsong (like these diagonal lines), and another that is diffuse and irregular, representing background noise. When scanning thousands of separated components, clustering and aligning parts for modeling bird vocalizations is a core challenge. Using techniques like time shifting (dynamic time warping) and extracting features such as dominant frequency sweeps, durations, and temporal patterns can aid in grouping similar vocalizations and linking them to bird identity, location, or labels. Integrating this pipeline with BSS is non-trivial: handling large sets of high-dimensional separated data, ensuring synchronization with associated metadata, optimizing computation time, and managing exploratory clustering/labeling all pose engineering challenges. Building software that allows efficient, modular integration of signal processing, clustering, and manual or semi-supervised annotation will be demanding but is very feasible using current computing architectures and open-source audio analysis libraries.[^7_1][^7_2][^7_3] <span style="display:none">[^7_4]</span> <div align="center">⁂</div> [^7_1]: https://www.nature.com/articles/s44384-025-00025-6 [^7_2]: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210x.13357 [^7_3]: https://www.nature.com/articles/s41467-020-20539-9 [^7_4]: image.jpg
