菜单
Use when applying filters (lowpass, highpass, bandpass, A-weighting), normalizing signals, resampling, trimming, adding fades, computing RMS trends, calculating sound level (dB, A-weighting), or computing psychoacoustic metrics (loudness, roughness, sharpness) with wandas.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | wandas-signal-processing |
| description | Use when applying filters (lowpass, highpass, bandpass, A-weighting), normalizing signals, resampling, trimming, adding fades, computing RMS trends, calculating sound level (dB, A-weighting), or computing psychoacoustic metrics (loudness, roughness, sharpness) with wandas. |
ChannelFrame の時間域処理。各メソッドは新しいフレームを返す（イミュータブル）。
scipy.signal.butter 等の直接呼び出し禁止。必ず wandas メソッドを使う。.plot() / .describe() を使う。plt.plot(frame.data) は禁止。| メソッド | シグネチャ | 返り値 |
|---|---|---|
.high_pass_filter | (cutoff: float, order: int = 4) | ChannelFrame |
.low_pass_filter | (cutoff: float, order: int = 4) | ChannelFrame |
.band_pass_filter | (low_cutoff: float, high_cutoff: float, order: int = 4) | ChannelFrame |
.a_weighting | () | ChannelFrame |
.normalize | (norm: float = inf, axis: int = -1, threshold=None, fill=None) | ChannelFrame |
.remove_dc | () | ChannelFrame |
.resampling | (target_sr: float) | ChannelFrame |
.trim | (start: float = 0, end: float = None) | ChannelFrame |
.fix_length | (length: int = None, duration: float = None) | ChannelFrame |
.fade | (fade_ms: float = 50) | ChannelFrame |
.hpss_harmonic | (kernel_size: int = 31) | ChannelFrame |
.hpss_percussive | (kernel_size: int = 31) | ChannelFrame |
| メソッド | シグネチャ | 返り値 | 注意 |
|---|---|---|---|
.sound_level | (freq_weighting: str = "Z", time_weighting: str = "Fast", dB: bool = False) | ChannelFrame | dB=True で dB 換算。ch_units=['Pa'] 設定時のみ正しい dB SPL |
.rms_trend | (frame_length: int = 2048, hop_length: int = 512, dB: bool = False, Aw: bool = False) | ChannelFrame | フレームごとの時変 RMS |
freq_weighting オプション: "A", "C", "Z"（フラット）
time_weighting オプション: "Fast"（125ms）, "Slow"（1s）
| メソッド | シグネチャ | 返り値 | 単位 |
|---|---|---|---|
.loudness_zwtv | (field_type: str = "free") | ChannelFrame | sone（ISO 532-1） |
.loudness_zwst | (field_type: str = "free") | NDArrayReal | sone（スカラー値） |
.roughness_dw | (overlap: float = 0.5) | ChannelFrame | asper |
.roughness_dw_spec | (overlap: float = 0.5) | RoughnessFrame | asper/Bark |
.sharpness_din | (weighting: str = "din", field_type: str = "free") | ChannelFrame | acum |
.sharpness_din_st | (weighting: str = "din", field_type: str = "free") | NDArrayReal | acum（スカラー値） |
⚠️ loudness_zwst と sharpness_din_st は フレームではなく NDArrayReal を返す。.plot() はチェーンできない。
field_type オプション: "free"（自由音場）, "diffuse"（拡散音場）
weighting オプション: "din", "aures", "bismarck", "fastl"
import wandas as wd
signal = wd.read_wav("noisy.wav")
cleaned = (signal
.high_pass_filter(cutoff=50) # 低周波ノイズを除去
.low_pass_filter(cutoff=8000) # 高周波ノイズを除去
.normalize() # ピーク正規化
.fade(fade_ms=10)) # エッジをスムーズ化
cleaned.describe()
import wandas as wd
import numpy as np
# 物理単位付きフレーム（ch_units='Pa' で参照値 2e-5 が自動設定）
frame = wd.from_numpy(signal_pa, sampling_rate=sr, ch_units=["Pa"])
spl = frame.sound_level(freq_weighting="A", time_weighting="Fast", dB=True)
print(f"Leq: {np.mean(spl.data):.1f} dB(A)")
print(f"Lmax: {np.max(spl.data):.1f} dB(A)")
import wandas as wd
sig = wd.read_wav("audio.wav") # 44.1kHz 以上推奨
# 時変指標（フレームとして返る）
loudness = sig.loudness_zwtv(field_type="free") # sone
roughness = sig.roughness_dw(overlap=0.5) # asper
sharpness = sig.sharpness_din(weighting="din") # acum
loudness.plot(title="Time-varying Loudness [sone]")
roughness.plot(title="Time-varying Roughness [asper]")
sharpness.plot(title="Time-varying Sharpness [acum]")
# 定常値（スカラー）
loudness_val = sig.loudness_zwst(field_type="free") # NDArrayReal
sharpness_val = sig.sharpness_din_st(weighting="din") # NDArrayReal
print(f"Loudness: {loudness_val} sone")
print(f"Sharpness: {sharpness_val} acum")
rms = frame.rms_trend(frame_length=2048, hop_length=512, dB=True, Aw=True)
rms.plot(title="A-weighted RMS Trend")
import wandas as wd
signal = wd.read_wav("music.wav")
harmonic = signal.hpss_harmonic()
percussive = signal.hpss_percussive()
harmonic.describe()
| 間違い | 正解 |
|---|---|
.band_pass_filter(low=100, high=5000) | .band_pass_filter(low_cutoff=100, high_cutoff=5000) |
.fft().a_weighting() | a_weighting() は ChannelFrame にのみ適用可能 |
normalize() が RMS 正規化 | デフォルトはピーク正規化（norm=inf） |
| カットオフをナイキスト以上に設定 | cutoff < sampling_rate / 2 を確認 |
loudness_zwst().plot() を呼ぶ | loudness_zwst は NDArrayReal を返す。フレームではない |
sound_level(dB=True) の値がおかしい | ch_units=['Pa'] 未設定時は参照値 1.0 で計算される |
examples/workflows.md — コピペで動くワークフロー集references/filters_api.md — フィルタ・前処理の詳細 APIreferences/psychoacoustic_api.md — 心理音響指標の詳細 APIUse when auditing screenshots or live UI for cognitive load, usability friction, and heuristic violations.
Use when reproducing or preventing runtime-only Webview UI bugs with real Chromium checks, Playwright smoke specs, static HTML-pattern linting, or dogfooding the UI smoke layers against a suspected regression.
Use when analyzing audio or vibration signals end-to-end, generating Jupyter Notebook analysis reports, comparing multiple measurement conditions, evaluating noise or sound quality, detecting anomalies in sensor data, or performing adaptive signal investigation driven by hypothesis and findings with wandas.
Use when starting with wandas, loading audio or sensor data from WAV or CSV files, creating signals from NumPy arrays, understanding ChannelFrame and other frame types, inspecting signal metadata, or setting up physical units (Pa, m/s²) for dB calculations.
Use when performing FFT, STFT, Welch PSD estimation, 1/N octave band analysis, coherence, cross-spectral density, or transfer function analysis with wandas.
Use when plotting waveforms, frequency spectra, spectrograms, octave band charts, roughness heatmaps, overlaying multiple signals on the same axes, or configuring describe() with frequency range and colormap settings with wandas.