Reference

hann_window(
    window_length::Int, ::Type{T} = Float32; periodic::Bool = true,
) where T <: Real

Hann window function (ref: Window function § Hann and Hamming windows - Wikipedia).

$w[n] = \frac{1}{2}[1 - \cos(\frac{2 \pi n}{N - 1})]$

Where $N$ is the window length.

julia> lineplot(hann_window(100); width=30, height=10)
     ┌──────────────────────────────┐
   1 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⠚⠉⠉⠉⠢⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡔⠁⠀⠀⠀⠀⠀⠘⢄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠞⠀⠀⠀⠀⠀⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⢀⡎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢣⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⡎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢦⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⢀⠞⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢆⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⢀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢇⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢦⠀⠀⠀⠀│
     │⠀⠀⠀⢠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠣⡀⠀⠀│
   0 │⣀⣀⠔⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⢤⣀│
     └──────────────────────────────┘
     ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀100⠀

Arguments:

• window_length::Int: Size of the window.
• ::Type{T}: Elemet type of the window.

Keyword Arguments:

• periodic::Bool: If true (default), returns a window to be used as periodic function. If false, return a symmetric window.

  Following always holds:

julia> N = 256;

julia> hann_window(N; periodic=true) ≈ hann_window(N + 1; periodic=false)[1:end - 1]
true

julia> hann_window(N) ≈ hamming_window(N; α=0.5f0, β=0.5f0)
true

Returns:

Vector of length window_length and eltype T.

hamming_window(
    window_length::Int, ::Type{T} = Float32; periodic::Bool = true,
    α::T = T(0.54), β::T = T(0.46),
) where T <: Real

Hamming window function (ref: Window function § Hann and Hamming windows - Wikipedia). Generalized version of hann_window.

$w[n] = \alpha - \beta \cos(\frac{2 \pi n}{N - 1})$

Where $N$ is the window length.

julia> lineplot(hamming_window(100); width=30, height=10)
     ┌──────────────────────────────┐
   1 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠚⠉⠉⠉⠢⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠎⠁⠀⠀⠀⠀⠀⠈⢢⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⢣⡀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⠀⢰⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⠀⣠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⡀⠀⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡄⠀⠀⠀⠀⠀│
     │⠀⠀⠀⠀⠀⡰⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠱⡀⠀⠀⠀⠀│
     │⠀⠀⠀⢀⠴⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⢄⠀⠀⠀│
     │⠀⢀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⣀⠀│
   0 │⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉│
     └──────────────────────────────┘
     ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀100⠀

Arguments:

• window_length::Int: Size of the window.
• ::Type{T}: Elemet type of the window.

Keyword Arguments:

• periodic::Bool: If true (default), returns a window to be used as periodic function. If false, return a symmetric window.

  Following always holds:

julia> N = 256;

julia> hamming_window(N; periodic=true) ≈ hamming_window(N + 1; periodic=false)[1:end - 1]
true

• α::Real: Coefficient α in the equation above.
• β::Real: Coefficient β in the equation above.

Returns:

Vector of length window_length and eltype T.

stft(x;
    n_fft::Int, hop_length::Int = n_fft ÷ 4, window = nothing,
    center::Bool = true, normalized::Bool = false,
)

Short-time Fourier transform (STFT).

The STFT computes the Fourier transform of short overlapping windows of the input, giving frequency components of the signal as they change over time.

$Y[\omega, m] = \sum_{k = 0}^{N - 1} \text{window}[k] \text{input}[m \times \text{hop length} + k] \exp(-j \frac{2 \pi \omega k}{\text{n fft}})$

where $N$ is the window length, $\omega$ is the frequency $0 \le \omega < \text{n fft}$ and $m$ is the index of the sliding window.

Arguments:

• x: Input, must be either a 1D time sequence ((L,) shape) or a 2D batch of time sequence ((L, B) shape).

Keyword Arguments:

• n_fft::Int: Size of Fourier transform.
• hop_length::Int: Distance between neighboring sliding window frames.
• window: Optional window function to apply. Must be 1D vector 0 < length(window) ≤ n_fft. If window is shorter than n_fft, it is padded with zeros on both sides. If nothing (default), then no window is applied.
• center::Bool: Whether to pad input on both sides so that $t$-th frame is centered at time $t \times \text{hop length}$. Padding is done with pad_reflect function.
• normalized::Bool: Whether to return normalized STFT, i.e. multiplied with $\text{n fft}^{-0.5}$.

Returns:

Complex array of shape (n_fft, n_frames, B), where B is the optional batch dimension.

istft(y;
    n_fft::Int, hop_length::Int = n_fft ÷ 4, window = nothing,
    center::Bool = true, normalized::Bool = false,
    return_complex::Bool = false,
    original_length::Union{Nothing, Int} = nothing,
)

Inverse Short-time Fourier Transform.

Return the least squares estimation of the original signal

Arguments:

• y: Input complex array in the (n_fft, n_frames, B) shape. Where B is the optional batch dimension.

Keyword Arguments:

• n_fft::Int: Size of Fourier transform.
• hop_length::Int: Distance between neighboring sliding window frames.
• window: Window function that was applied to the input of stft. If nothing (default), then no window was applied.
• center::Bool: Whether input to stft was padded on both sides so that $t$-th frame is centered at time $t \times \text{hop length}$. Padding is done with pad_reflect function.
• normalized::Bool: Whether input to stft was normalized.
• return_complex::Bool: Whether the output should be complex, or if the input should be assumed to derive from a real signal and window.
• original_length::Union{Nothing, Int}: Optional size of the first dimension of the input to stft. Helps restoring the exact stft input size. Otherwise, the array might be a bit shorter.

power_to_db(s; ref::Real = 1f0, amin::Real = 1f-10, top_db::Real = 80f0)

Convert a power spectrogram (amplitude squared) to decibel (dB) units.

Arguments

• s: Input power.
• ref: Scalar w.r.t. which the input is scaled.
• amin: Minimum threshold for s.
• top_db: Threshold the output at top_db below the peak: max.(s_db, maximum(s_db) - top_db).

Returns

s_db ~= 10 * log10(s) - 10 * log10(ref)

db_to_power(s_db; ref::Real = 1f0)

Inverse of power_to_db.

melscale_filterbanks(;
    n_freqs::Int, n_mels::Int, sample_rate::Int,
    fmin::Float32 = 0f0, fmax::Float32 = Float32(sample_rate ÷ 2))

Create triangular Mel scale filter banks (ref: Mel scale - Wikipedia). Each column is a filterbank that highlights its own frequency.

Arguments:

• n_freqs::Int: Number of frequencies to highlight.
• n_mels::Int: Number of mel filterbanks.
• sample_rate::Int: Sample rate of the audio waveform.
• fmin::Float32: Minimum frequency in Hz.
• fmax::Float32: Maximum frequency in Hz.

Returns:

Filterbank matrix of shape (n_freqs, n_mels) where each column is a filterbank.

julia> n_mels = 8;

julia> fb = melscale_filterbanks(; n_freqs=200, n_mels, sample_rate=16000);

julia> plot = lineplot(fb[:, 1]);

julia> for i in 2:n_mels
           lineplot!(plot, fb[:, i])
       end

julia> plot
     ┌────────────────────────────────────────┐
   1 │⠀⡀⢸⠀⢸⠀⠀⣧⠀⠀⢸⡄⠀⠀⠀⣷⠀⠀⠀⠀⠀⣷⠀⠀⠀⠀⠀⠀⢀⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⡇⢸⡆⢸⡇⠀⣿⠀⠀⡜⡇⠀⠀⢰⠋⡆⠀⠀⠀⢰⠁⡇⠀⠀⠀⠀⠀⡸⠀⢣⠀⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⣿⢸⡇⡇⡇⢰⠹⡄⠀⡇⢱⠀⠀⢸⠀⢣⠀⠀⠀⡜⠀⢸⡀⠀⠀⠀⢀⠇⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⠀│
     │⠀⣿⡇⡇⡇⡇⢸⠀⡇⢀⠇⠸⡀⠀⡇⠀⠸⡀⠀⢀⠇⠀⠀⢇⠀⠀⠀⡸⠀⠀⠀⠸⡄⠀⠀⠀⠀⠀⠀⠀│
     │⢠⢻⡇⡇⡇⢱⢸⠀⢇⢸⠀⠀⡇⢀⠇⠀⠀⡇⠀⢸⠀⠀⠀⠸⡀⠀⢠⠇⠀⠀⠀⠀⢱⠀⠀⠀⠀⠀⠀⠀│
     │⢸⢸⡇⢱⡇⢸⡇⠀⢸⢸⠀⠀⢣⢸⠀⠀⠀⢸⠀⡇⠀⠀⠀⠀⢇⠀⡜⠀⠀⠀⠀⠀⠈⢇⠀⠀⠀⠀⠀⠀│
     │⢸⢸⡇⢸⠀⢸⡇⠀⢸⡇⠀⠀⢸⡎⠀⠀⠀⠈⣶⠁⠀⠀⠀⠀⠸⣤⠃⠀⠀⠀⠀⠀⠀⠘⡆⠀⠀⠀⠀⠀│
     │⢸⠀⡇⢸⠀⠀⡇⠀⠀⡇⠀⠀⠀⡇⠀⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⢱⡀⠀⠀⠀⠀│
     │⢸⢸⡇⢸⠀⢸⡇⠀⢸⡇⠀⠀⢸⢇⠀⠀⠀⢀⠿⡀⠀⠀⠀⠀⢰⠛⡄⠀⠀⠀⠀⠀⠀⠀⠀⢣⠀⠀⠀⠀│
     │⢸⢸⡇⡸⡇⢸⡇⠀⢸⢸⠀⠀⡜⢸⠀⠀⠀⢸⠀⡇⠀⠀⠀⠀⡎⠀⢣⠀⠀⠀⠀⠀⠀⠀⠀⠘⡆⠀⠀⠀│
     │⢸⢸⡇⡇⡇⡸⢸⠀⡎⢸⠀⠀⡇⠈⡆⠀⠀⡇⠀⢸⠀⠀⠀⢰⠁⠀⠘⡆⠀⠀⠀⠀⠀⠀⠀⠀⠸⡄⠀⠀│
     │⡇⢸⡇⡇⡇⡇⢸⠀⡇⠈⡆⢰⠁⠀⡇⠀⢰⠁⠀⠈⡆⠀⠀⡎⠀⠀⠀⢱⠀⠀⠀⠀⠀⠀⠀⠀⠀⢣⠀⠀│
     │⡇⢸⢸⡇⡇⡇⠸⣰⠃⠀⡇⡸⠀⠀⢸⠀⡜⠀⠀⠀⢣⠀⢸⠁⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠈⢇⠀│
     │⡇⡇⢸⠇⢸⡇⠀⣿⠀⠀⢣⡇⠀⠀⠸⣄⠇⠀⠀⠀⠸⡀⡇⠀⠀⠀⠀⠀⢱⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡄│
   0 │⣇⣇⣸⣀⣸⣀⣀⣟⣀⣀⣸⣃⣀⣀⣀⣿⣀⣀⣀⣀⣀⣿⣀⣀⣀⣀⣀⣀⣈⣇⣀⣀⣀⣀⣀⣀⣀⣀⣀⣱│
     └────────────────────────────────────────┘
     ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀200⠀

spectrogram(waveform;
    pad::Int = 0, n_fft::Int, hop_length::Int, window,
    center::Bool = true, power::Real = 2.0,
    normalized::Bool = false, window_normalized::Bool = false,
)

Create a spectrogram or a batch of spectrograms from a raw audio signal.

Arguments

• pad::Int: Then amount of padding to apply on both sides.
• window_normalized::Bool: Whether to normalize the waveform by the window’s L2 energy.
• power::Real: Exponent for the magnitude spectrogram (must be ≥ 0) e.g., 1 for magnitude, 2 for power, etc. If 0, complex spectrum is returned instead.

See stft for other arguments.

Returns

Spectrogram in the shape (T, F, B), where T is the number of window hops and F = n_fft ÷ 2 + 1.