Add peak_dbfs/rms_dbfs to AudioStatistics, normalize_to_dbfs/apply_gain_db to AudioProcessing, and new dithering feature

[Copilot]

Extends the operation trait surface with dBFS-aware statistics and processing helpers, and adds a new dithering feature module following the same end-to-end pattern as existing feature-gated modules (dynamic-range, etc.).

statistics feature — AudioStatistics

• peak_dbfs() -> f64 — 20·log₁₀(peak) via amplitude_to_db; -80 dB floor for silence
• rms_dbfs() -> f64 — 20·log₁₀(rms); paired dBFS companion to peak_dbfs

Both are default implementations built on the existing peak() and rms() methods.

processing feature — AudioProcessing

• normalize_to_dbfs(target: f64) — converts target dBFS → linear, delegates to normalize(NormalizationConfig::peak(...))
• apply_gain_db(db: f64) — converts dB → linear scale factor, delegates to scale()

Both are default implementations; no changes to existing normalize/scale logic.

New dithering feature

Mirrors the dynamic-range module layout exactly: feature flag → submodule → trait in traits.rs → config types in types.rs → re-exports in mod.rs and lib.rs.

Cargo.toml: dithering = ["random-generation"], added to full and full_no_plotting.

NoiseShape enum (types.rs):

• Flat — pure TPDF (two uniform draws subtracted)
• FWeighted — TPDF with first-order highpass shaping H(z) = 1 − 0.5·z⁻¹; per-channel state, no dynamic dispatch

AudioDithering trait (traits.rs + dithering.rs):

• dither(shape: NoiseShape) -> Self — noise amplitude is 1 LSB of native type (1/MAX for integers; ~2⁻²³ for floats). Works in the normalised f64 domain via convert_to/convert_from.
• requantize(bits: u32) -> AudioSampleResult<Self> — snaps each sample to a 2^(bits−1)-level grid in normalised f64 space; valid range [1, 32]. Distinct from to_type::<i16>() which rescales — this reduces precision within the same container type.

use audio_samples::{AudioDithering, AudioStatistics, AudioProcessing, NoiseShape};

// dBFS measurement
let peak_db = audio.peak_dbfs();   // e.g. -3.0
let rms_db  = audio.rms_dbfs();

// dBFS-targeted normalization and gain
let loud = audio.normalize_to_dbfs(-6.0)?;
let quiet = audio.apply_gain_db(-12.0);

// Dither before bit-depth reduction
let out = audio
    .dither(NoiseShape::FWeighted)
    .requantize(16)?;

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