diff --git a/CLAUDE.md b/CLAUDE.md
index 6273da4..56c488f 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -2,15 +2,17 @@
 
 **Always update this file when making notable changes** (new commands, new presets, architectural decisions, scoring changes, dependency additions).
 
+**Always update README.md** when changing presets, CLI flags, output format, or any user-facing behavior.
+
 ## Project
 
-`noisekit` is a `uvx`-compatible Python CLI that generates degraded speech datasets from clean HuggingFace corpora. It simulates three real-world audio degradation scenarios — telecommunication (G.711 calls), bad audio encoding (low-bitrate codecs), and noisy environments (real ambient noise) — for ASR noise-robustness benchmarking. A `clean_reference` control completes the catalog.
+`noisekit` is a `uvx`-compatible Python CLI that generates degraded speech datasets from clean HuggingFace corpora. It simulates seven atomic audio degradation scenarios — telecom (G.711 calls), bad audio encoding (low-bitrate codecs), noisy environments (real ambient noise), far-field reverb, transmission dropout, and clipping distortion — plus compound multi-condition scenarios built by chaining atomic presets. Designed for ASR noise-robustness benchmarking. A `clean_reference` control completes the catalog.
 
 ## Package Management
 
 Use **UV** for everything: `uv add`, `uv run`, `uv sync`. Never use pip directly.
 
-Key runtime dependencies: `audiomentations>=0.38`, `lameenc>=1.4` (pure-Python MP3 encoder used by `Mp3Compression` in `telecommunication` and `bad_audio_encoding`; no system ffmpeg needed), `torchmetrics>=1.7.0` (NISQA scoring — downloads ~50 MB model weights to `~/.torchmetrics/NISQA/` on first use).
+Key runtime dependencies: `audiomentations>=0.38`, `lameenc>=1.4` (pure-Python MP3 encoder used by `Mp3Compression` in `telecom` and `bad_audio_encoding`; no system ffmpeg needed), `torchmetrics>=1.7.0` (NISQA scoring — downloads ~50 MB model weights to `~/.torchmetrics/NISQA/` on first use), `pyroomacoustics` (room acoustics simulation for `reverb_far_field` — now a core dependency, no extra install needed).
 
 ## Architecture
 
@@ -61,29 +63,78 @@ transforms:
 
 Built-in presets:
 
-| Preset               | Scenario                                     | Bandwidth           | PESQ mode | Target MOS |
-| -------------------- | -------------------------------------------- | ------------------- | --------- | ---------- |
-| `clean_reference`    | Minimal gain normalization (PESQ ceiling)    | full                | WB 16 kHz | 4.0-4.5    |
-| `telecommunication`  | G.711 call + low-bitrate MP3 codec artifacts | 300-3400 Hz @ 8 kHz | NB 8 kHz  | 2.0-3.5    |
-| `bad_audio_encoding` | Aggressive low-bitrate MP3 (16-32 kbps)      | 80-7500 Hz @ 16 kHz | WB 16 kHz | 1.5-2.5    |
-| `noisy_environment`  | Real ambient noise via `AddBackgroundNoise`  | up to 8-12 kHz      | WB 16 kHz | 2.0-3.5    |
+### Atomic Presets
+
+| Preset                 | Scenario                                              | Bandwidth           | PESQ mode | Target MOS |
+| ---------------------- | ----------------------------------------------------- | ------------------- | --------- | ---------- |
+| `clean_reference`      | Minimal gain normalization (PESQ ceiling)             | full                | WB 16 kHz | 4.0-4.5    |
+| `telecom`              | G.711 call + low-bitrate MP3 codec artifacts          | 300-3400 Hz @ 8 kHz | NB 8 kHz  | 2.0-3.5    |
+| `bad_audio_encoding`   | Aggressive low-bitrate MP3 (16-32 kbps)               | 80-7500 Hz @ 16 kHz | WB 16 kHz | 1.5-2.5    |
+| `noisy_environment`    | Real ambient noise via `AddBackgroundNoise`           | up to 8-12 kHz      | WB 16 kHz | 2.0-3.5    |
+| `clipping_distortion`  | Microphone overload / ADC saturation (`ClippingDistortion` 10-25%) | full | WB 16 kHz | 2.0-3.5    |
+| `transmission_dropout` | VoIP packet loss: 1-3 silent dropout windows          | full                | WB 16 kHz | 1.5-3.0    |
+| `reverb_far_field`     | Far-field reverberant room via `RoomSimulator`                              | full | WB 16 kHz | 2.0-3.5 |
+
+`telecom` and any compound preset ending with `telecom` use the 8 kHz PESQ NB scoring split (see below). All other presets score in PESQ WB at 16 kHz.
+
+### Compound Presets
+
+Compound presets chain two or more atomic presets together. Noise is added first (acoustic environment), then codec/dropout (digital processing of the already-degraded signal).
+
+| Preset             | Chain                                     | Requires      | PESQ mode | Target MOS |
+| ------------------ | ----------------------------------------- | ------------- | --------- | ---------- |
+| `noisy_telecom`    | `noisy_environment` → `telecom`           | `--noise-dir` | NB 8 kHz  | 1.5-2.5    |
+| `reverb_noisy`     | `reverb_far_field` → `noisy_environment`  | `--noise-dir` | WB 16 kHz | 1.0-2.5    |
+| `clipping_telecom` | `clipping_distortion` → `telecom`         | —             | NB 8 kHz  | 1.0-2.5    |
+
+### Compound Preset YAML Format
+
+A preset can use `chain:` instead of `transforms:` to apply multiple atomic presets sequentially:
 
-`telecommunication` uses the 8 kHz PESQ NB scoring split (see below). All other presets score in PESQ WB at 16 kHz.
+```yaml
+name: my_compound
+description: "..."
+chain:
+  - atomic_preset_a
+  - atomic_preset_b
+```
+
+Rules:
+- `chain` and `transforms` are mutually exclusive.
+- Chained entries must be names of built-in atomic presets (no nesting chains).
+- `${NOISE_DIR}` resolution and the PESQ NB scoring split are detected automatically across the full concatenated chain.
+- `reverb_far_field` uses `pyroomacoustics` (bundled as a core dependency — no extra install needed).
 
 ### Why no white noise
 
 The catalog deliberately avoids `AddGaussianSNR` — white Gaussian noise sounds artificial and doesn't reflect real production audio. Instead:
 
-- `telecommunication` and `bad_audio_encoding` rely on `Mp3Compression` at 16-32 kbps for realistic codec smearing/pre-echo.
+- `telecom` and `bad_audio_encoding` rely on `Mp3Compression` at 16-32 kbps for realistic codec smearing/pre-echo.
 - `noisy_environment` uses `AddBackgroundNoise` over a user-supplied WAV corpus (MUSAN/DEMAND/FSD50K), so the noise floor matches the real environment you care about.
 
 ## PESQ Scoring — Important Design Decision
 
-For `telecommunication`, PESQ is computed at **8 kHz narrowband** on the audio **before** the final `Resample(16000)` restoration step. Output WAV files are still saved at 16 kHz.
+For `telecom`, PESQ is computed at **8 kHz narrowband** on the audio **before** the final `Resample(16000)` restoration step. Output WAV files are still saved at 16 kHz.
 
 **Why:** Computing PESQ NB by downsampling the 16 kHz output (8k→16k→8k round-trip) collapses all telephony scores to ~1.1 regardless of noise level. Scoring at the 8 kHz intermediate stage gives proper stratification.
 
-**BitCrush + Normalize:** `telecommunication` inserts `Normalize(p=1.0)` immediately before `BitCrush`. HuggingFace speech datasets (e.g., FLEURS) often have very low peak amplitude (~0.001-0.02). At 8-bit depth the quantization step is 0.0078 — a peak below one step rounds the entire signal to zero. Normalizing to ±1 before quantization ensures all 256 levels are used.
+**BitCrush + Normalize:** `telecom` inserts `Normalize(p=1.0)` immediately before `BitCrush`. HuggingFace speech datasets (e.g., FLEURS) often have very low peak amplitude (~0.001-0.02). At 8-bit depth the quantization step is 0.0078 — a peak below one step rounds the entire signal to zero. Normalizing to ±1 before quantization ensures all 256 levels are used.
+
+## Input Normalization — Global Pipeline Decision
+
+`pipeline.py` peak-normalizes every input sample to amplitude 1.0 immediately after resampling to 16 kHz, before any preset transforms run:
+
+```python
+peak = np.abs(ref_16k).max()
+if peak > 1e-9:
+    ref_16k = ref_16k / peak
+```
+
+**Why:** HuggingFace datasets often have peaks as low as 0.001–0.02. Without normalization, `AddBackgroundNoise` (relative SNR mode) scales noise proportional to that tiny signal RMS — both speech and noise end up inaudible, and 16-bit PCM quantization noise dominates. Peak normalization guarantees all presets receive a full-scale signal.
+
+**Safety:** The same normalized `ref_16k` is used as both the transform input and the PESQ/SNR reference, so all quality metrics remain valid relative comparisons. The mid-chain `Normalize` inside `telecom.yaml` (before `BitCrush`) is still needed separately — the bandpass filter removes energy and that step re-normalizes before quantization.
+
+**`noisy_environment` also pre-normalizes:** `noisy_environment.yaml` adds a `Normalize` as its first transform. This handles the `reverb_noisy` compound case: `RoomSimulator` can attenuate the signal by ~10× at large mic distances; without the mid-chain normalize, `AddBackgroundNoise` would see the attenuated level and mix noise too quietly. All compound presets using `noisy_environment` inherit this fix automatically.
 
 `transforms.py` auto-detects this split: if the last transform is `Resample(16000)`, it creates a `scoring` Compose (all-but-last) alongside the `full` Compose.
 
@@ -97,11 +148,11 @@ Uses `datasets` with `Audio(decode=False)` + manual `soundfile` decoding — avo
 
 ```json
 {
-  "file_name": "audio/common_voice_en_23136613_telecommunication.wav",
+  "file_name": "audio/common_voice_en_23136613_telecom.wav",
   "source": "common_voice_en_23136613.mp3",
   "dataset": "google/fleurs",
   "language": "en-US",
-  "preset": "telecommunication",
+  "preset": "telecom",
   "transcript": "...",
   "snr_db": 1.8,
   "pesq_mos": 2.86,
@@ -126,16 +177,40 @@ uv run noisekit list-presets --verbose
 uv run noisekit generate \
   --dataset google/fleurs \
   --config en_us --split test \
-  --samples 3 --presets clean_reference telecommunication bad_audio_encoding \
+  --samples 3 --presets clean_reference telecom bad_audio_encoding \
   --output ./test_out --seed 42
 cat test_out/metadata.jsonl
 
+# New atomic presets — no external dependencies
+uv run noisekit generate \
+  --dataset google/fleurs --config en_us --split test \
+  --samples 3 --presets clipping_distortion transmission_dropout \
+  --no-nisqa --output ./test_atomic --seed 42
+
 # noisy_environment — auto-downloads MUSAN noise-only clips on first run
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
   --samples 3 --presets noisy_environment \
   --output ./test_noise --seed 42
 
+# Compound presets (auto-downloads MUSAN noise on first run)
+uv run noisekit generate \
+  --dataset google/fleurs --config en_us --split test \
+  --samples 3 --presets noisy_telecom \
+  --no-nisqa --output ./test_compound --seed 42
+
+# clipping_telecom — no noise dir needed
+uv run noisekit generate \
+  --dataset google/fleurs --config en_us --split test \
+  --samples 3 --presets clipping_telecom \
+  --no-nisqa --output ./test_clipping_telecom --seed 42
+
+# Far-field reverb
+uv run noisekit generate \
+  --dataset google/fleurs --config en_us --split test \
+  --samples 3 --presets reverb_far_field reverb_noisy \
+  --no-nisqa --output ./test_reverb --seed 42
+
 # noisy_environment with your own noise corpus (skips auto-download)
 uv run noisekit generate \
   --dataset google/fleurs --config en_us --split test \
@@ -144,6 +219,8 @@ uv run noisekit generate \
   --output ./test_noise --seed 42
 ```
 
-Expected PESQ spread: clean ~4.6, telecommunication ~2.5-3.5 (NB), bad_audio_encoding ~1.5-2.5 (WB), noisy_environment ~1.0-2.5 (WB).
+Expected PESQ spread: clean ~4.6, telecom ~2.5-3.5 (NB), bad_audio_encoding ~1.5-2.5 (WB), noisy_environment ~1.0-2.5 (WB), clipping_distortion ~2.0-3.5 (WB), transmission_dropout ~1.5-3.0 (WB), reverb_far_field ~2.0-3.5 (WB).
+
+Compound preset PESQ: noisy_telecom ~1.5-2.5 (NB), clipping_telecom ~1.0-2.5 (NB), reverb_noisy ~1.0-2.5 (WB).
 
 Expected NISQA spread: clean ~4.0-4.5, degraded presets ~1.5-3.0. NISQA model weights (~50 MB) are downloaded on first run.
diff --git a/README.md b/README.md
index 7a2c4f0..40d6bc5 100644
--- a/README.md
+++ b/README.md
@@ -10,22 +10,23 @@
 
 <br/>
 
-Generate noise-stratified speech datasets for ASR benchmark studies.
+Generate degraded speech datasets for noise-robust ASR benchmarking.
 
-Takes a clean speech-to-text dataset from HuggingFace, applies real-world degradation presets via [audiomentations](https://github.com/iver56/audiomentations), and scores each output with PESQ + SNR + NISQA — producing a JSONL manifest ready for noise-robustness benchmarking.
+Takes a clean HuggingFace speech dataset, applies real-world degradation presets via [audiomentations](https://github.com/iver56/audiomentations), and scores each output with PESQ, SNR, and NISQA, producing a JSONL manifest ready for noise-robustness benchmarking.
 
-Three scenarios are covered out of the box: **telecommunication** (G.711 + low-bitrate MP3 codec artifacts), **bad audio encoding** (aggressive low-bitrate compression), and **noisy environment** (real ambient noise from a user-supplied corpus).
+Seven atomic degradation scenarios are built in: telephony (G.711 + low-bitrate codec), wideband codec compression, ambient noise, clipping distortion, transmission dropout, and far-field reverb. Atomic presets compose into compound multi-condition scenarios.
+
+> [!NOTE]
+> Degradations are programmatically simulated. Scores may not generalize to genuine production recordings; validate final benchmarks on annotated real-world data.
 
 ## How it works
 
 ```mermaid
 flowchart LR
     A[("HuggingFace\nDataset")] --> B["noisekit generate"]
-    B --> C["telecommunication\nG.711 + MP3"]
-    B --> D["bad_audio_encoding\n16-32 kbps MP3"]
-    B --> E["noisy_environment\nReal ambient noise"]
-    B --> F["clean_reference\nControl"]
-    C & D & E & F --> G[("WAVs +\nmetadata.jsonl\nPESQ · SNR · NISQA")]
+    B --> C["7 atomic presets\ncodec · noise · reverb\ndropout · clipping"]
+    B --> D["3 compound presets\nmulti-condition chains"]
+    C & D --> E[("WAVs + metadata.jsonl\nPESQ · SNR · NISQA")]
 ```
 
 ## Install
@@ -55,7 +56,7 @@ uvx noisekit generate \
   --config en_us \
   --split test \
   --samples 300 \
-  --presets telecommunication bad_audio_encoding \
+  --presets telecom bad_audio_encoding \
   --output ./benchmark_dataset \
   --seed 42
 ```
@@ -76,7 +77,7 @@ Output:
 benchmark_dataset/
 ├── metadata.jsonl          # one entry per generated file (AudioFolder format)
 └── audio/
-    ├── sample_0000_telecommunication.wav
+    ├── sample_0000_telecom.wav
     ├── sample_0001_bad_audio_encoding.wav
     └── ...
 ```
@@ -92,11 +93,11 @@ Each `metadata.jsonl` entry:
 
 ```json
 {
-  "file_name": "audio/sample_0042_telecommunication.wav",
+  "file_name": "audio/sample_0042_telecom.wav",
   "source": "common_voice_en_23136613.mp3",
   "dataset": "google/fleurs",
   "language": "en-US",
-  "preset": "telecommunication",
+  "preset": "telecom",
   "transcript": "the cat sat on the mat",
   "snr_db": 5.2,
   "pesq_mos": 2.78,
@@ -130,19 +131,46 @@ uvx noisekit list-presets --verbose   # show full transform stack
 
 ## Presets
 
-Four built-in presets — three real-world scenarios plus a clean control. None use synthetic white noise; codec artifacts and real ambient recordings produce the degradation instead.
+Ten built-in presets: seven atomic scenarios, three compound multi-condition presets, and a clean reference control. None use synthetic white noise; codec artifacts, real ambient recordings, and room simulation produce the degradation instead.
+
+### Atomic presets
+
+| Preset                 | Description                                                              | PESQ       |
+| ---------------------- | ------------------------------------------------------------------------ | ---------- |
+| `clean_reference`      | Minimal processing (PESQ ceiling / control)                              | 4.0-4.5    |
+| `telecom`              | G.711-style call: 8 kHz bandpass + 8-bit BitCrush + 16-32 kbps MP3 codec | NB 2.0-3.5 |
+| `bad_audio_encoding`   | Wideband audio crushed by 16-32 kbps MP3 compression                     | WB 1.5-2.5 |
+| `noisy_environment`    | Real ambient noise from `--noise-dir` mixed in at SNR 5-15 dB            | WB 1.0-2.5 |
+| `clipping_distortion`  | Microphone overload: clips the loudest 10-25% of samples                 | WB 2.0-3.5 |
+| `transmission_dropout` | VoIP packet loss: 1-3 silent dropout windows (60-180 ms each)            | WB 1.5-3.0 |
+| `reverb_far_field`     | Far-field room reverb at 1-3 m mic distance                              | WB 2.0-3.5 |
 
-| Preset               | Description                                                              | PESQ       |
-| -------------------- | ------------------------------------------------------------------------ | ---------- |
-| `clean_reference`    | Minimal processing (PESQ ceiling / control)                              | 4.0-4.5    |
-| `telecommunication`  | G.711-style call: 8 kHz bandpass + 8-bit BitCrush + 16-32 kbps MP3 codec | NB 2.0-3.5 |
-| `bad_audio_encoding` | Wideband audio crushed by 16-32 kbps MP3 compression                     | WB 1.5-2.5 |
-| `noisy_environment`  | Real ambient noise from `--noise-dir` mixed in at SNR 3-20 dB            | WB 1.0-2.5 |
+`telecom` is scored with PESQ narrowband at 8 kHz (before the final upsample); all other presets are scored wideband at 16 kHz.
 
-`telecommunication` is scored with PESQ narrowband at 8 kHz (before the final upsample); all other presets are scored wideband at 16 kHz.
+All atomic presets require no noise corpus. All dependencies, including `pyroomacoustics` (used by `reverb_far_field`), are bundled with no extra install needed.
 
 `noisy_environment` requires `--noise-dir` pointing at a directory of background-noise WAVs (e.g. MUSAN, DEMAND, FSD50K). If omitted, noisekit auto-downloads a small MUSAN noise-only subset (~120 MB) from HuggingFace on first use.
 
+### Compound presets
+
+Compound presets chain two atomic presets together. Noise is applied first (acoustic environment), then codec or dropout (digital processing on the already-degraded signal).
+
+| Preset             | Chain                                    | Requires      | PESQ       |
+| ------------------ | ---------------------------------------- | ------------- | ---------- |
+| `noisy_telecom`    | `noisy_environment` → `telecom`          | `--noise-dir` | NB 1.5-2.5 |
+| `clipping_telecom` | `clipping_distortion` → `telecom`        | (none)        | NB 1.0-2.5 |
+| `reverb_noisy`     | `reverb_far_field` → `noisy_environment` | `--noise-dir` | WB 1.0-2.5 |
+
+You can also define your own compound preset with a `chain:` key in a YAML file:
+
+```yaml
+name: my_compound
+description: "Noisy environment then telephony codec"
+chain:
+  - noisy_environment
+  - telecom
+```
+
 ### Custom presets
 
 Pass your own YAML file with `--preset-file`:
@@ -179,10 +207,10 @@ transforms:
     p: 1.0
 ```
 
-Any transform from [audiomentations](https://github.com/iver56/audiomentations) is supported. Use `${NOISE_DIR}` as a placeholder for `--noise-dir` inside your preset YAML.
+Any transform from [audiomentations](https://github.com/iver56/audiomentations) is supported. Use `${NOISE_DIR}` as a placeholder for `--noise-dir` inside your preset YAML. Use `chain:` instead of `transforms:` to compose built-in atomic presets sequentially.
 
 ## Requirements
 
 - Python ≥ 3.10
 - [uv](https://docs.astral.sh/uv/) for `uvx` usage
-- No system dependencies — MP3 encoding uses pure-Python `lameenc`, no ffmpeg needed
+- No system dependencies: MP3 encoding uses pure-Python `lameenc`, no ffmpeg needed
diff --git a/assets/banner.svg b/assets/banner.svg
index 3021680..3b5ab1e 100644
--- a/assets/banner.svg
+++ b/assets/banner.svg
@@ -9,7 +9,7 @@
   <text x="40" y="72" font-family="ui-monospace, 'Cascadia Code', 'Fira Code', monospace" font-size="52" font-weight="700" fill="#f8fafc" letter-spacing="-1">noisekit</text>
 
   <!-- Tagline -->
-  <text x="40" y="108" font-family="-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif" font-size="17" fill="#94a3b8" letter-spacing="0.2">Noise-stratified speech datasets for ASR robustness</text>
+  <text x="40" y="108" font-family="-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif" font-size="17" fill="#94a3b8" letter-spacing="0.2">Noisy speech datasets for ASR robustness</text>
 
   <!-- Waveform group, right side -->
   <g transform="translate(540, 80)">
diff --git a/noisekit/cli.py b/noisekit/cli.py
index a3e60ff..bb9c011 100644
--- a/noisekit/cli.py
+++ b/noisekit/cli.py
@@ -10,7 +10,7 @@
 
 app = typer.Typer(
     name="noisekit",
-    help="Generate noise-stratified speech datasets for ASR benchmark studies.",
+    help="Generate noisy speech datasets for ASR benchmark studies.",
     add_completion=False,
 )
 console = Console()
@@ -44,6 +44,12 @@ def generate(
     ] = True,
 ) -> None:
     """Generate a degraded speech dataset by applying audio presets to a clean source dataset."""
+    console.print(
+        "[bold yellow]⚠  Synthetic pipeline[/bold yellow] — outputs approximate real-world degradation "
+        "but are not a substitute for true annotated production audio. "
+        "Validate final benchmarks on real degraded recordings.",
+        style="yellow",
+    )
     from .pipeline import run_generate
 
     resolved_output = output if output is not None else Path("./output") / datetime.now().strftime("%Y-%m-%d_%H%M%S")
@@ -100,7 +106,10 @@ def list_presets(
         table.add_column("Transforms")
 
     for p in presets:
-        transforms_str = " → ".join(f"{t['type']}(p={t.get('p', 1.0)})" for t in p.get("transforms", []))
+        if "chain" in p:
+            transforms_str = "chain: " + " → ".join(p["chain"])
+        else:
+            transforms_str = " → ".join(f"{t['type']}(p={t.get('p', 1.0)})" for t in p.get("transforms", []))
         if verbose:
             table.add_row(p["name"], p["description"], transforms_str)
         else:
diff --git a/noisekit/pipeline.py b/noisekit/pipeline.py
index a91040c..7167b56 100644
--- a/noisekit/pipeline.py
+++ b/noisekit/pipeline.py
@@ -58,6 +58,9 @@ def run_generate(
         ref_array, ref_sr, transcript = extract_audio_and_text(sample)
         language = extract_language(sample, config)
         ref_16k = _resample_to_16k(ref_array, ref_sr)
+        peak = np.abs(ref_16k).max()
+        if peak > 1e-9:
+            ref_16k = ref_16k / peak
 
         raw_path = sample.get("audio", {}).get("path") or ""
         raw_stem = Path(raw_path).stem if raw_path else f"sample_{i:04d}"
diff --git a/noisekit/presets/clipping_distortion.yaml b/noisekit/presets/clipping_distortion.yaml
new file mode 100644
index 0000000..daa5bac
--- /dev/null
+++ b/noisekit/presets/clipping_distortion.yaml
@@ -0,0 +1,13 @@
+name: clipping_distortion
+description: "Microphone overload / ADC saturation: clips 10-25% of peak samples. PESQ WB target: 2.0-3.5"
+transforms:
+  - type: ClippingDistortion
+    parameters:
+      min_percentile_threshold: 10
+      max_percentile_threshold: 25
+    p: 1.0
+  - type: Gain
+    parameters:
+      min_gain_db: -3.0
+      max_gain_db: 3.0
+    p: 0.5
diff --git a/noisekit/presets/clipping_telecom.yaml b/noisekit/presets/clipping_telecom.yaml
new file mode 100644
index 0000000..441eeea
--- /dev/null
+++ b/noisekit/presets/clipping_telecom.yaml
@@ -0,0 +1,5 @@
+name: clipping_telecom
+description: "Clipped microphone signal over a phone line: ADC saturation + G.711 codec. PESQ NB target: 1.0-2.5"
+chain:
+  - clipping_distortion
+  - telecom
diff --git a/noisekit/presets/noisy_environment.yaml b/noisekit/presets/noisy_environment.yaml
index ff2e234..544bf40 100644
--- a/noisekit/presets/noisy_environment.yaml
+++ b/noisekit/presets/noisy_environment.yaml
@@ -1,6 +1,9 @@
 name: noisy_environment
 description: "Real-world ambient noise (cafe/street/office) mixed in at variable SNR. Requires --noise-dir. PESQ WB target: 1.0–2.5"
 transforms:
+  - type: Normalize
+    parameters: {}
+    p: 1.0
   - type: AddBackgroundNoise
     parameters:
       sounds_path: ${NOISE_DIR}
diff --git a/noisekit/presets/noisy_telecom.yaml b/noisekit/presets/noisy_telecom.yaml
new file mode 100644
index 0000000..f049be8
--- /dev/null
+++ b/noisekit/presets/noisy_telecom.yaml
@@ -0,0 +1,5 @@
+name: noisy_telecom
+description: "Telephony call in a noisy environment: ambient noise + G.711 codec. Requires --noise-dir. PESQ NB target: 1.5-2.5"
+chain:
+  - noisy_environment
+  - telecom
diff --git a/noisekit/presets/reverb_far_field.yaml b/noisekit/presets/reverb_far_field.yaml
new file mode 100644
index 0000000..11c6431
--- /dev/null
+++ b/noisekit/presets/reverb_far_field.yaml
@@ -0,0 +1,18 @@
+name: reverb_far_field
+description: "Far-field recording in a reverberant room (lecture hall / open office). Requires pyroomacoustics. PESQ WB target: 2.0-3.5"
+transforms:
+  - type: RoomSimulator
+    parameters:
+      min_size_x: 5.0
+      max_size_x: 10.0
+      min_size_y: 5.0
+      max_size_y: 10.0
+      min_size_z: 2.5
+      max_size_z: 4.0
+      min_absorption_value: 0.05
+      max_absorption_value: 0.20
+      min_mic_distance: 1.0
+      max_mic_distance: 3.0
+      use_ray_tracing: false
+      leave_length_unchanged: true
+    p: 1.0
diff --git a/noisekit/presets/reverb_noisy.yaml b/noisekit/presets/reverb_noisy.yaml
new file mode 100644
index 0000000..cea6450
--- /dev/null
+++ b/noisekit/presets/reverb_noisy.yaml
@@ -0,0 +1,5 @@
+name: reverb_noisy
+description: "Far-field reverberant recording with ambient noise. Requires --noise-dir and pyroomacoustics. PESQ WB target: 1.0-2.5"
+chain:
+  - reverb_far_field
+  - noisy_environment
diff --git a/noisekit/presets/telecommunication.yaml b/noisekit/presets/telecom.yaml
similarity index 97%
rename from noisekit/presets/telecommunication.yaml
rename to noisekit/presets/telecom.yaml
index c164a29..a922879 100644
--- a/noisekit/presets/telecommunication.yaml
+++ b/noisekit/presets/telecom.yaml
@@ -1,4 +1,4 @@
-name: telecommunication
+name: telecom
 description: "G.711-style call: 8 kHz codec + 300–3400 Hz bandpass + low-bitrate MP3 artifacts. PESQ NB target: 2.0–3.5"
 transforms:
   - type: Resample
diff --git a/noisekit/presets/transmission_dropout.yaml b/noisekit/presets/transmission_dropout.yaml
new file mode 100644
index 0000000..4859e40
--- /dev/null
+++ b/noisekit/presets/transmission_dropout.yaml
@@ -0,0 +1,21 @@
+name: transmission_dropout
+description: "VoIP/streaming packet loss: 1-3 silent dropout windows per utterance. PESQ WB target: 1.5-3.0"
+transforms:
+  - type: TimeMask
+    parameters:
+      min_band_part: 0.02
+      max_band_part: 0.06
+      fade_duration: 0.002
+    p: 1.0
+  - type: TimeMask
+    parameters:
+      min_band_part: 0.02
+      max_band_part: 0.06
+      fade_duration: 0.002
+    p: 0.8
+  - type: TimeMask
+    parameters:
+      min_band_part: 0.02
+      max_band_part: 0.06
+      fade_duration: 0.002
+    p: 0.5
diff --git a/noisekit/transforms.py b/noisekit/transforms.py
index b3be0f1..cc472e0 100644
--- a/noisekit/transforms.py
+++ b/noisekit/transforms.py
@@ -6,6 +6,7 @@
 import audiomentations
 import numpy as np
 import yaml
+from audiomentations.core.transforms_interface import BaseWaveformTransform
 
 
 class _SRTrackingCompose:
@@ -19,7 +20,7 @@ class _SRTrackingCompose:
     the effective rate from the length ratio before calling the next transform.
     """
 
-    def __init__(self, transforms: list[audiomentations.BaseWaveformTransform]) -> None:
+    def __init__(self, transforms: list[BaseWaveformTransform]) -> None:
         self._transforms = transforms
 
     def __call__(self, samples: np.ndarray, sample_rate: int) -> np.ndarray:
@@ -67,10 +68,42 @@ def preset_requires_noise_dir(name: str, preset_file: Path | None = None) -> boo
         for v in t.get("parameters", {}).values():
             if v == _NOISE_DIR_PLACEHOLDER:
                 return True
-    return False
+    return any(preset_requires_noise_dir(chained_name, preset_file) for chained_name in cfg.get("chain", []))
 
 
-def _make_transform(t: dict, noise_dir: Path | None = None) -> audiomentations.BaseWaveformTransform:
+def _collect_t_configs(cfg: dict, preset_file: Path | None, noise_dir: Path | None) -> list[dict]:
+    """Resolve a preset config to a flat list of transform dicts.
+
+    Atomic presets return cfg['transforms'] directly. Compound presets
+    (chain: [name, ...]) load each named preset's transforms and concatenate
+    them. Nesting chains inside chains is not supported.
+    """
+    if "chain" in cfg and "transforms" in cfg:
+        raise ValueError(
+            f"Preset '{cfg.get('name', '?')}' defines both 'chain' and 'transforms'. Use one or the other."
+        )
+    if "transforms" in cfg:
+        return list(cfg["transforms"])
+    if "chain" not in cfg:
+        raise ValueError(f"Preset '{cfg.get('name', '?')}' has neither 'transforms' nor 'chain' key.")
+    presets_dir = Path(__file__).parent / "presets"
+    combined: list[dict] = []
+    for chained_name in cfg["chain"]:
+        if preset_file is not None:
+            candidate = preset_file.parent / f"{chained_name}.yaml"
+            chained_path = candidate if candidate.exists() else presets_dir / f"{chained_name}.yaml"
+        else:
+            chained_path = presets_dir / f"{chained_name}.yaml"
+        if not chained_path.exists():
+            raise FileNotFoundError(f"Chained preset '{chained_name}' not found at {chained_path}.")
+        chained_cfg = yaml.safe_load(chained_path.read_text())
+        if "chain" in chained_cfg:
+            raise ValueError(f"Chained preset '{chained_name}' is itself a compound preset. Nesting not supported.")
+        combined.extend(chained_cfg.get("transforms", []))
+    return combined
+
+
+def _make_transform(t: dict, noise_dir: Path | None = None) -> BaseWaveformTransform:
     cls_name = t["type"]
     if not hasattr(audiomentations, cls_name):
         raise ValueError(
@@ -94,7 +127,7 @@ def load_preset(
         raise FileNotFoundError(f"Preset '{name}' not found. Run 'noisekit list-presets' to see available presets.")
 
     cfg = yaml.safe_load(path.read_text())
-    t_configs = cfg["transforms"]
+    t_configs = _collect_t_configs(cfg, preset_file, noise_dir)
 
     full = _SRTrackingCompose([_make_transform(t, noise_dir) for t in t_configs])
 
diff --git a/pyproject.toml b/pyproject.toml
index 8169282..956f233 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "noisekit"
 version = "0.1.0"
-description = "Generate noise-stratified speech datasets for ASR benchmark studies"
+description = "Generate noisy speech datasets for ASR benchmark studies"
 license = {text = "MIT"}
 requires-python = ">=3.10"
 dependencies = [
@@ -18,6 +18,7 @@ dependencies = [
     "soundfile>=0.12",
     "numpy>=1.26",
     "scipy>=1.13",
+    "pyroomacoustics>=0.7",
     "pyyaml>=6.0",
     "librosa>=0.10",
     "lameenc>=1.4",
diff --git a/tests/test_smoke.py b/tests/test_smoke.py
index a9e7736..27ac89e 100644
--- a/tests/test_smoke.py
+++ b/tests/test_smoke.py
@@ -17,9 +17,32 @@ def test_list_builtin_presets() -> None:
     from noisekit.transforms import list_builtin_presets
 
     presets = list_builtin_presets()
-    assert len(presets) == 4
+    assert len(presets) == 10
     names = {p["name"] for p in presets}
     assert "clean_reference" in names
-    assert "telecommunication" in names
+    assert "telecom" in names
     assert "bad_audio_encoding" in names
     assert "noisy_environment" in names
+    assert "clipping_distortion" in names
+    assert "transmission_dropout" in names
+    assert "reverb_far_field" in names
+    assert "noisy_telecom" in names
+    assert "reverb_noisy" in names
+    assert "clipping_telecom" in names
+
+
+def test_load_compound_preset_scoring_split(tmp_path) -> None:
+    import numpy as np
+    import soundfile as sf
+
+    from noisekit.transforms import load_preset
+
+    # AddBackgroundNoise scans sounds_path at construction — write a minimal WAV.
+    sf.write(tmp_path / "noise.wav", np.zeros(16000, dtype=np.float32), 16000)
+
+    # noisy_telecom chains noisy_environment → telecom.
+    # The concatenated transform list ends with Resample(16000), so the NB 8 kHz
+    # scoring split should be detected automatically.
+    pt = load_preset("noisy_telecom", noise_dir=tmp_path)
+    assert pt.scoring is not None, "noisy_telecom should inherit telecom's NB scoring split"
+    assert pt.scoring_sr == 8000, "scoring_sr should be 8000 from telecom's Resample"
diff --git a/uv.lock b/uv.lock
index 8cb00cd..20dec93 100644
--- a/uv.lock
+++ b/uv.lock
@@ -573,6 +573,44 @@ nvtx = [
     { name = "nvidia-nvtx", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
 ]
 
+[[package]]
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+version = "3.2.4"
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     { name = "pesq", specifier = ">=0.0.4" },
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