Simple Audio Update
| 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 spectrogram to reduce edge effects. |
| normalization | Normalizing the spectrogram values to a common range (e.g., [0, 1]) for easier comparison and analysis. |
| noise reduction | Removing noise from the spectrogram using techniques like spectral subtraction or wavelet denoising. |
| convert to spectrogram | 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 |
| mel frequency cepstral coefficients | Extracting features from the spectrogram that represent the human auditory system’s response to sound. |
| spectral centroid | Calculating the center of gravity of the spectrogram to describe the spectral distribution of energy. |
| band energy ratio | Computing the ratio of energy in different frequency bands (e.g., low, mid, high) to characterize the audio signal. |
| spectral roll-off | Measuring the frequency below which a certain percentage (e.g., 85%) of the total energy is contained. |
| onset detection | Calculating the rate of change of the spectral power density over time. |
| spectral flux | Comparing two spectrograms using the Euclidean distance metric to measure similarity. |
| euclidean distance | Measuring the cosine of the angle between two spectrogram vectors to assess similarity. |
| cosine similarity | Aligning two spectrograms in time to compare their shapes and structures. |
| dynamic time warping | Training an SVM classifier on a set of spectrograms to recognize patterns and classify new audio signals. |
| support vector machines | Classifying an unknown audio signal based on the similarity between its spectrogram and those in a labeled dataset. |
| k nearest neighbors | Searching for a known pattern or template within a spectrogram to detect specific events (e.g., speech, music). |
| template matching | Identifying patterns in the spectral shape of an audio signal to recognize events like applause or cheering. |
| spectral shape analysis | Detecting the onset of a sound event (e.g., drum hit, voice) by analyzing the spectrogram’s time-frequency structure. |
| onset detection | Analyzing the spectrogram to identify the rhythmic structure and beat of music. |
| beat tracking | Separating mixed audio signals into their individual sources using ICA techniques. |
| independent component analysis | Decomposing a spectrogram into its constituent parts (e.g., instruments, vocals) using NMF. |
| non-negative matrix factorization | Using deep neural networks to separate audio sources from a mixed signal. |
| deep learning-based source separation | Reducing noise in an audio signal by subtracting the noise spectrum from the original spectrogram. |
| spectral subtraction | Applying a Wiener filter to the spectrogram to reduce noise and enhance the audio signal. |
| wiener filtering | Using wavelet transforms to remove noise from the spectrogram. |
| de-noising using wavelet transform | Identifying chords in music by analyzing the spectrogram’s harmonic structure. |
| chord recognition | Determining the key of a song by analyzing the spectrogram’s spectral distribution. |
| key detection | Classifying music into different mood or emotion categories based on spectrogram features. |
| mood and emotion recognition | Recognizing spoken words by analyzing the spectrogram’s acoustic features. |
| speaker identification | Identifying speakers based on their unique spectrogram characteristics. |
| emotion recognition | Detecting emotions in speech by analyzing spectrogram features like pitch, intensity, and spectral shape. |
| bird song analysis | Analyzing the spectrograms of bird songs to identify species, behavior, or habitat. |
| whale vocalization analysis | Studying the spectrograms of whale vocalizations to understand their communication patterns. |
| analyze | analyze the transcription |
| help | get all possible commands from simple audio |
| print what is in simple audio |
Filed under: Uncategorized - @ June 2, 2026 1:29 pm