Source: https://jmrplens.github.io/phonometry/guides/multichannel/ # Multichannel and Performance Most real measurement sessions produce more than one channel: the two ears of a dummy head, the microphone pair of an intensity probe, the several positions of a room survey, the capsules of a beamforming array. The convention for all of them is one array of shape `(channels, samples)` processed in a single call: every function runs along the last (time) axis and preserves the leading channel axis, so each row is analyzed exactly as if it were the only one. That per-channel guarantee is normative, not just convenient. The band filters applied to each row are the IEC 61260-1 designs and the weightings and detector ballistics are the IEC 61672-1 ones, unchanged from the single-channel path; the vectorization batches the arithmetic across rows (one filter design, one SciPy call) and never mixes them. This is the textbook procedure for multiple data records (Bendat & Piersol 2010, §10.4.2): analyze each record individually first, and compute anything joint as a separate, explicit step. Use this page when your channels are parallel recordings on the same clock and you want per-channel spectra or levels. When the question is *between* channels (what is the delay from A to B, how much of B is explained by A), that is cross-channel analysis: see [Correlation and delay](https://jmrplens.github.io/phonometry/guides/correlation-delay/) and [Multiple and partial coherence](https://jmrplens.github.io/phonometry/guides/miso-coherence/), the implementations of the Bendat & Piersol cross-correlation and multiple-input models. ## A stereo analysis in one call *Simultaneous analysis of a Stereo signal: Left Channel (Pink Noise) vs Right Channel (Log Sine Sweep).*

Show the code for this figure

```python import matplotlib.pyplot as plt import numpy as np from scipy.signal import chirp from phonometry import metrology # Stereo test signal: pink noise left, logarithmic sine sweep right fs, duration = 48000, 5 t = np.linspace(0, duration, fs * duration, endpoint=False) rng = np.random.default_rng(42) spec = np.fft.rfft(rng.standard_normal(t.size)) spec[1:] /= np.sqrt(np.arange(1, spec.size)) # 1/f shaping: pink noise left = np.fft.irfft(spec, t.size) right = chirp(t, f0=50, t1=duration, f1=10000, method="logarithmic") x = np.stack([left, right]) # (2, n_samples) spl, freq = metrology.octave_filter(x, fs, fraction=3, limits=[20, 20000]) fig, axes = plt.subplots(2, 1, figsize=(9, 7), sharex=True) for ax, levels, name in zip(axes, spl, ["Left: pink noise", "Right: log sweep"]): ax.semilogx(freq, levels, marker="o", label=name) ax.set_ylabel("Level [dB]") ax.grid(True, which="both", alpha=0.3) ax.legend() axes[-1].set_xlabel("Frequency [Hz]") plt.show() ```

The convention is consistent across the whole library: time is always the **last axis**. This applies to `octave_filter`, `OctaveFilterBank`, `weighting_filter`, `time_weighting`, `leq`, `laeq`, `ln_levels` and `spectrogram`. ```python import numpy as np from phonometry import metrology # Two calibrated channels in Pa so the guide runs standalone fs = 48000 t = np.arange(fs) / fs left = 0.2 * np.sin(2 * np.pi * 1000 * t) right = 0.1 * np.sin(2 * np.pi * 500 * t) stereo = np.stack([left, right]) # (2, n_samples) spl, freq = metrology.octave_filter(stereo, fs, fraction=3) # spl has shape (2, n_bands): one row per channel ``` ## Accepted shapes, at a glance | Input | Interpreted as | Typical output | | :--- | :--- | :--- | | `(n,)` 1D array | one channel | scalar level / `(bands,)` | | `(ch, n)` 2D array | `ch` channels, `n` samples each | `(ch,)` levels / `(ch, bands)` | | list of floats | one channel (converted) | as 1D | | `(ch, n)` into `spectrogram` | multichannel STFT-style | `(ch, bands, frames)` | Everything vectorizes across the leading channel axis: one filter design is applied to all channels in a single SciPy call, so 8 channels cost far less than 8 separate runs. Convention: **channels first**, like most DSP code (`soundfile` returns `(n, ch)`: transpose with `x.T`). ## Per-channel semantics Multichannel processing is strictly per channel: nothing is ever mixed, summed or averaged across the channel axis. Three consequences worth spelling out: - **Combining channels is your decision.** Levels come back one per channel. If you need an array-average level (for instance the position-averaged levels of the room-acoustics standards), combine energies yourself: `10 * np.log10(np.mean(10 ** (spl / 10), axis=0))`, never the arithmetic mean of the dB values (see [Levels](https://jmrplens.github.io/phonometry/reference/api/levels/levels/) for why). - **One `calibration_factor` means one sensitivity.** The scalar factor multiplies every channel, which is correct only if all channels share the same sensitivity. For an array of microphones with individual calibrations, scale the rows first, `x * factors[:, None]`, and leave `calibration_factor` at 1. - **Stateful classes keep one state per channel.** In block processing the state array matches the channel count and a change of channel count resets it; see [Block Processing](https://jmrplens.github.io/phonometry/guides/block-processing/). ## Performance: Vectorization and caching `OctaveFilterBank` is the tool for repeated or streaming analysis: one bank designs its filters once and applies them to every frame, and NumPy broadcasting covers all channels of a frame in one filtering call per band, with no Python loop over channels. ```python import numpy as np from phonometry import metrology # Two calibrated channels in Pa so the guide runs standalone fs = 48000 t = np.arange(fs) / fs left = 0.2 * np.sin(2 * np.pi * 1000 * t) right = 0.1 * np.sin(2 * np.pi * 500 * t) stereo = np.stack([left, right]) # (2, n_samples) bank = metrology.OctaveFilterBank(fs=48000, fraction=3, filter_type='butter') # Access computed properties # bank.freq (center), bank.freq_d (lower), bank.freq_u (upper), bank.sos (coefficients) # Process multiple signals efficiently stream = [stereo] # your sequence of multichannel frames for frame in stream: # detrend=True (default) removes DC offset to improve low-freq accuracy spl, freq = bank.filter(frame, detrend=True) ``` Additional performance notes: - **Design cache**: `octave_filter()` reuses filter bank designs across calls with identical parameters (LRU cache, 32 entries), so calling it in a loop does not redesign the bank each time. `OctaveFilterBank` gives you explicit control over the design lifetime. - **Multirate decimation**: low-frequency bands are filtered at a decimated rate, which is both faster and numerically more stable (see [Theory](https://jmrplens.github.io/phonometry/reference/theory/signal-analysis/)). - **Optional numba**: the `impulse` time weighting kernel is JIT-compiled when numba is installed (`pip install phonometry[perf]`). ## See also - [Correlation and delay](https://jmrplens.github.io/phonometry/guides/correlation-delay/): the cross-channel time-domain questions (delay, alignment) the per-channel path deliberately leaves to you. - [Multiple and partial coherence](https://jmrplens.github.io/phonometry/guides/miso-coherence/): which of several correlated channels actually drives a response (Bendat & Piersol Ch. 7). - [Block Processing](https://jmrplens.github.io/phonometry/guides/block-processing/): the streaming counterpart, with one filter state per channel. - [Levels](https://jmrplens.github.io/phonometry/reference/api/levels/levels/): the per-channel level metrics, and why dB values are combined energetically. ## References - Bendat, J. S., & Piersol, A. G. (2010). *Random data: Analysis and measurement procedures* (4th ed.). Wiley. [doi:10.1002/9781118032428](https://doi.org/10.1002/9781118032428). Section 10.4.2 (the procedure for analyzing multiple data records: individual per-record analysis first, joint cross-record analysis as a separate, deliberate step) and Chapter 7 (the multiple-input/output models that joint step leads to). - International Electrotechnical Commission. (2014). *Electroacoustics — Octave-band and fractional-octave-band filters — Part 1: Specifications* (IEC 61260-1:2014). [IEC webstore](https://webstore.iec.ch/en/publication/5063). The band definitions each channel is filtered with; the multichannel path batches them unchanged. - International Electrotechnical Commission. (2013). *Electroacoustics — Sound level meters — Part 1: Specifications* (IEC 61672-1:2013). [IEC webstore](https://webstore.iec.ch/en/publication/5708). The weighting and time-integration semantics applied per channel. ## Standards IEC 61260-1:2014, *Electroacoustics — Octave-band and fractional-octave-band filters — Part 1: Specifications*, and IEC 61672-1:2013, *Electroacoustics — Sound level meters — Part 1: Specifications* — multichannel support adds no normative content of its own: each channel is filtered, weighted and time-integrated exactly as the single-channel standards prescribe (see [Filter Banks](https://jmrplens.github.io/phonometry/guides/filter-banks/) and [Levels](https://jmrplens.github.io/phonometry/reference/api/levels/levels/)); the vectorization only batches the computation across the channel axis.