{"id":1755,"date":"2026-06-02T13:29:13","date_gmt":"2026-06-02T20:29:13","guid":{"rendered":"http:\/\/macdaddy4sure.ai\/?p=1755"},"modified":"2026-06-02T14:04:10","modified_gmt":"2026-06-02T21:04:10","slug":"simple-audio-update","status":"publish","type":"post","link":"http:\/\/macdaddy4sure.ai\/index.php\/2026\/06\/02\/simple-audio-update\/","title":{"rendered":"Simple Audio Update"},"content":{"rendered":"\n
simple audio<\/strong><\/td><\/td><\/tr>
add path<\/td>add a path to any audio file<\/td><\/tr>
convert wav<\/td>convert the audio file to wav<\/td><\/tr>
convert float<\/td>convert the audio file to floating point operation<\/td><\/tr>
transcribe audio<\/td>transcribe the audio<\/td><\/tr>
transcribe llm<\/td>transcribe audio with spectrogram<\/td><\/tr>
windowing<\/td>Applying a window function (e.g., Hamming, Hann) to the audio signal before computing the spectrogram to reduce edge effects.<\/td><\/tr>
normalization<\/td>Normalizing the spectrogram values to a common range (e.g., [0, 1]) for easier comparison and analysis.<\/td><\/tr>
noise reduction<\/td>Removing noise from the spectrogram using techniques like spectral subtraction or wavelet denoising.<\/td><\/tr>
convert to spectrogram<\/td>since spectrograms are images and audio is a float, we can import them into a multimodal language model and do math and processes with them<\/td><\/tr>
mel frequency cepstral coefficients<\/td>Extracting features from the spectrogram that represent the human auditory system’s response to sound.<\/td><\/tr>
spectral centroid<\/td>Calculating the center of gravity of the spectrogram to describe the spectral distribution of energy.<\/td><\/tr>
band energy ratio<\/td>Computing the ratio of energy in different frequency bands (e.g., low, mid, high) to characterize the audio signal.<\/td><\/tr>
spectral roll-off<\/td>Measuring the frequency below which a certain percentage (e.g., 85%) of the total energy is contained.<\/td><\/tr>
onset detection<\/td>Calculating the rate of change of the spectral power density over time.<\/td><\/tr>
spectral flux<\/td>Comparing two spectrograms using the Euclidean distance metric to measure similarity.<\/td><\/tr>
euclidean distance<\/td>Measuring the cosine of the angle between two spectrogram vectors to assess similarity.<\/td><\/tr>
cosine similarity<\/td>Aligning two spectrograms in time to compare their shapes and structures.<\/td><\/tr>
dynamic time warping<\/td>Training an SVM classifier on a set of spectrograms to recognize patterns and classify new audio signals.<\/td><\/tr>
support vector machines<\/td>Classifying an unknown audio signal based on the similarity between its spectrogram and those in a labeled dataset.<\/td><\/tr>
k nearest neighbors<\/td>Searching for a known pattern or template within a spectrogram to detect specific events (e.g., speech, music).<\/td><\/tr>
template matching<\/td>Identifying patterns in the spectral shape of an audio signal to recognize events like applause or cheering.<\/td><\/tr>
spectral shape analysis<\/td>Detecting the onset of a sound event (e.g., drum hit, voice) by analyzing the spectrogram’s time-frequency structure.<\/td><\/tr>
onset detection<\/td>Analyzing the spectrogram to identify the rhythmic structure and beat of music.<\/td><\/tr>
beat tracking<\/td>Separating mixed audio signals into their individual sources using ICA techniques.<\/td><\/tr>
independent component analysis<\/td>Decomposing a spectrogram into its constituent parts (e.g., instruments, vocals) using NMF.<\/td><\/tr>
non-negative matrix factorization<\/td>Using deep neural networks to separate audio sources from a mixed signal.<\/td><\/tr>
deep learning-based source separation<\/td>Reducing noise in an audio signal by subtracting the noise spectrum from the original spectrogram.<\/td><\/tr>
spectral subtraction<\/td>Applying a Wiener filter to the spectrogram to reduce noise and enhance the audio signal.<\/td><\/tr>
wiener filtering<\/td>Using wavelet transforms to remove noise from the spectrogram.<\/td><\/tr>
de-noising using wavelet transform<\/td>Identifying chords in music by analyzing the spectrogram’s harmonic structure.<\/td><\/tr>
chord recognition<\/td>Determining the key of a song by analyzing the spectrogram’s spectral distribution.<\/td><\/tr>
key detection<\/td>Classifying music into different mood or emotion categories based on spectrogram features.<\/td><\/tr>
mood and emotion recognition<\/td>Recognizing spoken words by analyzing the spectrogram’s acoustic features.<\/td><\/tr>
speaker identification<\/td>Identifying speakers based on their unique spectrogram characteristics.<\/td><\/tr>
emotion recognition<\/td>Detecting emotions in speech by analyzing spectrogram features like pitch, intensity, and spectral shape.<\/td><\/tr>
bird song analysis<\/td>Analyzing the spectrograms of bird songs to identify species, behavior, or habitat.<\/td><\/tr>
whale vocalization analysis<\/td>Studying the spectrograms of whale vocalizations to understand their communication patterns.<\/td><\/tr>
analyze<\/td>analyze the transcription<\/td><\/tr>
help<\/td>get all possible commands from simple audio<\/td><\/tr>
print<\/td>print what is in simple audio<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

simple audio add path add a path to any audio file convert wav convert the audio file to wav convert float convert the audio file to floating point operation transcribe audio transcribe the audio transcribe llm transcribe audio with spectrogram windowing Applying a window function (e.g., Hamming, Hann) to the audio signal before computing the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1755","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1755","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/comments?post=1755"}],"version-history":[{"count":4,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1755\/revisions"}],"predecessor-version":[{"id":1760,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1755\/revisions\/1760"}],"wp:attachment":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/media?parent=1755"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/categories?post=1755"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/tags?post=1755"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}