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enhance explanations in README, make tcd output more clear

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README.md
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@ -19,274 +19,322 @@ Careful! Dragons ahead!
`tcd` *can* and absolutely *will* delete your data. Don't blindly use -a, and
please read at least the --help information and *understand* what -a does.
How it works
------------
---
### 1. Signal acquisition
How it works (the short version)
--------------------------------
The program opens the file with libavformat, selects the first audio stream,
decodes up to `--duration` (default 60) seconds of audio (or the entire file
when `--full` is used), and converts every sample to 32-bit float PCM.
### 2. Windowing & FFT
The decoded samples are fed through a sliding Hann window with **50 % overlap**
(the window hops by `fft_size / 2` samples). Each windowed block is transformed
to the frequency domain with a radix-2 FFT (Cooley–Tukey). Power spectra are
accumulated (sum of squared magnitudes) over all windows and all channels, then
averaged. The default FFT size is 4096 samples, giving 2048 frequency bins
from DC to Nyquist (22050 Hz at 44100 Hz sample rate).
### 3. Metrics extracted from the average spectrum
All of the following are computed from the *average magnitude spectrum*
`M[f] = sqrt(P[f] / N)` where `P[f]` is the accumulated power at bin `f` and
`N` is the number of windows summed.
#### Cutoff frequency
Searched from Nyquist downward. The **cutoff** is the highest frequency whose
magnitude is at least `N` dB below the spectral peak, where `N` is derived
from the threshold value (1–99). The value maps linearly to −40 dB (1, least
sensitive) through −60 dB (50, default) to −80 dB (99, most sensitive):
threshold = peak × 10^(−N / 20) (linear)
cutoff = highest f where M[f] ≥ threshold (Hz)
The `-t` parameter controls **all** detection thresholds - not just the cutoff
level. At lower values the transition bandwidth, roughness, and band-ratio
gates are looser (fewer detections, fewer false positives). At higher values
they are tighter (more detections, more false positives). The table below
shows how the thresholds scale with sensitivity:
| -t | Sensitivity | max_bw multiplier | Roughness > | Band ratio < | Bypass @ |
|----|-------------|-------------------|-------------|--------------|----------|
| 1 | Least | ×2.0 | 0.70 / 0.53 / 0.35 | 0.85 / 0.80 | ≥1.00 |
| 50 | Default | ×1.0 | 0.40 / 0.30 / 0.20 | 0.90 / 0.85 | ≥0.99 |
| 99 | Most | ×0.25 | 0.10 / 0.08 / 0.05 | 0.95 / 0.90 | ≥0.98 |
Lossy encoders place their lowpass cutoff somewhere below Nyquist. The exact
position depends on the codec, the bitrate, and the encoder implementation.
#### Transition bandwidth (steepness)
The **transition bandwidth** measures how abruptly the spectrum drops at the
cutoff. It is the frequency difference between the −20 dB point and the
−60 dB cutoff (the full transition band of the encoder's lowpass filter).
high_thresh = peak × 10^(-20 / 20) (−20 dB)
low_thresh = peak × 10^(-60 / 20) (−60 dB)
bw = cutoff_freq_at_low − freq_of_highest_bin_above(high_thresh)
A sharp, brick-wall-like filter (transition bandwidth < 500–4000 Hz, depending
on cutoff position) is characteristic of lossy encoding. Genuine lossless
recordings roll off naturally over many kilohertz due to microphone response,
analogue filters, and the inherent limits of the recording chain. Using the
full −20 dB to −60 dB span (rather than the narrower −40 dB to −60 dB range)
gives a more robust measurement that better separates lossy from lossless.
#### Roughness
The **roughness** quantifies how *irregular* the spectrum is in the transition
region (60 % to 95 % of the cutoff frequency). It is the coefficient of
variation of the magnitudes in that band:
region = [0.60 × cutoff, 0.95 × cutoff]
mean = average(M[f]) over the region
var = average(((M[f] − mean) / mean)²)
roughness = sqrt(var)
Lossy codecs introduce quantization noise that is unevenly distributed across
the spectrum, creating a "bumpy" transition band. Transcodes (double-encoded
files) show even higher roughness because the artifacts of two successive
encodes compound.
#### Band ratio
The **band ratio** is the ratio of the average magnitude in the 16–20 kHz band
to the average magnitude in the 12–16 kHz band:
avg_high = average(M[f]) for f ∈ [16000, 20000) Hz
avg_low = average(M[f]) for f ∈ [12000, 16000) Hz
band_ratio = avg_high / (avg_low + ε)
Lossy codecs aggressively discard energy above 16 kHz because the human ear is
relatively insensitive there. A low band ratio (< 0.85–0.90) is a strong
marker of lossy origins.
#### Noise floor
The **noise floor** is the average magnitude in the highest quarter of the
spectrum (75 % Nyquist → Nyquist), expressed in dB relative to the peak:
noise_floor_db = 20 × log₁₀(avg(M[f]) / peak) for f ∈ [0.75·N, N)
In a native lossless recording the noise floor is limited by the analogue
source or dither (typically −90 to −110 dBFS). Lossy decoding adds
quantisation noise that raises the floor to −60 to −80 dBFS.
tcd decodes your audio file, converts it to the frequency domain (like a
graph showing how much energy exists at each frequency), then measures several
properties of that frequency graph. Each property is a clue about whether the
audio was produced by a lossy encoder. Combined, these clues give a verdict.
---
Decision logic
--------------
What tcd displays and what each number means
---------------------------------------------
The tool distinguishes two scenarios based on the codec of the input file.
Here is the example output you saw:
### A. Input is a lossy codec (mp3, aac, vorbis, opus, wma, ac3, …)
```
File: ./08 You Got Me.mp3
Format: mp3
Bitrate: 320 kbps
Sample rate: 44100 Hz
Channels: 2
Windows: 13550
Peak: 47.5 dBFS
Cutoff: 20790 / 22050 Hz = 94.3%
Steepness: 20209 Hz
Roughness: 0.323
Band ratio: 0.555
Noise floor: -56.4 dB
Verdict: NATIVE
Verdict Info: bandwidth used=94.3% (20790/22050 Hz), expected≥90% for 320 kbps
```
The cutoff is compared against the expected minimum for the file's *stated*
Each metric is explained below.
---
### Cutoff (20790 / 22050 Hz = 94.3%)
**What it is:** The highest frequency where the audio still has measurable
energy. Everything above this point is silence or noise.
**The Nyquist ceiling:** Digital audio is made of snapshots (samples). For CD
quality (44100 snapshots per second), there is a hard limit: you cannot store
a frequency higher than half the snapshot rate = **22050 Hz**. This is called
the *Nyquist frequency*. It is a physical ceiling — higher frequencies simply
cannot exist.
**How lossy encoding changes it:** MP3 and other lossy codecs deliberately cut
off high frequencies to save space. The cutoff gets lower as the bitrate drops:
| Bitrate | Typical cutoff | Audio quality impact |
|---------|---------------|----------------------|
| 320 kbps | ≥20000 Hz (≥90%) | Keeps almost all audible high end |
| 256 kbps | ≥19000 Hz (≥86%) | Still very clean |
| 192 kbps | ~17500 Hz (~79%) | Moderate high-end roll-off |
| 128 kbps | ~16000 Hz (~73%) | Noticeable treble loss |
| 96 kbps | ~13000 Hz (~59%) | Significant high-end missing |
| 64 kbps | ~11000 Hz (~50%) | Sounds dull, heavily filtered |
**What 94.3% means for your file:** 20790 / 22050 = 94.3%. The cutoff is very
close to the theoretical maximum. This is what we expect from a 320 kbps
encode. If this same file showed 54% (~12000 Hz), it would mean the treble
was chopped off by an aggressive low-bitrate encoder, and someone just
re-encoded it at 320 kbps — the cutoff is permanent and cannot be restored.
That would be an **UPSCALED** file.
---
### Steepness
**What it measures:** How abruptly the sound drops off *at* the cutoff point.
tcd measures this as the frequency gap between the −20 dB point (still loud)
and the −60 dB cutoff (essentially silent). A narrow gap = a sharp drop.
**The analogy:** Imagine the frequency graph as a mountain ridge. A lossless
recording rolls off like a natural hillside — gradual, smooth, taking
thousands of Hz to go from loud to silent. A lossy encoder's lowpass filter
creates a cliff — a near-vertical drop from audible signal to nothing.
**What the number means:** Steepness is the width (in Hz) of that drop zone.
The smaller the number, the sharper the cliff:
| Steepness | What it looks like | Likely origin |
|-----------|-------------------|---------------|
| <500 Hz | Brick-wall drop | Lossy encoder (MP3, AAC) |
| 500–2000 Hz | Fairly sharp | Could be lossy or aggressive production filter |
| 2000–5000 Hz | Moderate | Might be natural |
| >5000 Hz | Gentle slope | Natural acoustic roll-off (lossless) |
To understand steepness, imagine a guitar string being plucked. The sound
naturally fades across many frequencies — the harmonics near the top end of
your hearing get quieter and quieter over a broad range. This is a gentle
slope. Now imagine someone put a pair of scissors on the frequency spectrum
and cut everything above a certain note. That sharp edge — the difference
between "still audible" and "completely gone" in just a few hundred Hz — is
what lossy compression does. The steepness number tells you how sharp that
scissor cut was.
---
### Roughness (0.323)
**What it measures:** How "bumpy" or "irregular" the spectrum looks just
before the cutoff point.
**The analogy:** Lossy encoding introduces quantization noise — tiny
rounding errors that are unevenly distributed across frequencies. In the
frequency graph, this looks like a jagged, bumpy line instead of a smooth
one. Think of it like a dirt road vs a paved highway: lossless audio is
smooth, lossy audio is bumpy. Double-encoded audio (a transcode) is even
bumpier because the errors from two encodings stack on top of each other.
**What the number means:**
| Roughness | What it looks like | Likely origin |
|-----------|-------------------|---------------|
| <0.15 | Very smooth | Natural/lossless |
| 0.15–0.30 | Slightly bumpy | Could be lossy single encode |
| 0.30–0.50 | Clearly bumpy | Lossy single encode, or borderline transcode |
| >0.50 | Very jagged | Almost certainly a transcode |
---
### Band ratio (0.555)
**What it measures:** How much high-frequency energy (16–20 kHz) remains
compared to mid-high energy (12–16 kHz).
**Why it matters:** Human hearing is least sensitive above 16 kHz. Lossy
encoders exploit this by spending almost no bits on those frequencies. The
result is that the 16–20 kHz region is much quieter than the 12–16 kHz region.
In native recordings, this drop is modest; in lossy/transcoded material, it
is severe.
**What the number means:** Band ratio = energy in 16–20 kHz band ÷ energy in
12–16 kHz band. A ratio of 1.0 means both bands are equally loud. A ratio of
0.5 means the top band is half as loud.
| Band ratio | What it means |
|------------|---------------|
| >0.85 | Healthy high end — likely native lossless |
| 0.70–0.85 | Mild roll-off — could be lossy or natural |
| 0.50–0.70 | Significant high-end loss — likely lossy |
| <0.50 | Severe high-end loss — almost certainly lossy or transcoded |
---
### Noise floor (-56.4 dB)
**What it measures:** The average noise level in the highest quarter of the
frequency range (roughly 16500–22050 Hz).
**The analogy:** Imagine listening in a quiet room — the background hiss is
very low. Now imagine that same room with a fan running — the background
noise rises. A lossy encoder introduces quantization noise that raises the
"background hiss" in the high frequencies.
**What the number means:** This is measured in decibels (dB). More negative =
quieter (better). Less negative = noisier (worse):
| Noise floor | What it means |
|-------------|---------------|
| −90 to −110 dB | Very clean — native lossless |
| −70 to −90 dB | Moderate — could be lossy or quiet lossless |
| −50 to −70 dB | Noisy — likely lossy |
| >−50 dB | Very noisy — almost certainly lossy or transcoded |
---
How tcd combines these clues into a verdict
---------------------------------------------
tcd does not rely on any single metric. It combines them in stages, like a
detective building a case.
### Scenario 1: The input file is lossy (MP3, AAC, etc.)
The file already claims to be lossy. The question is: was it *originally*
encoded at the stated bitrate, or was it re-encoded from a lower bitrate?
**The check:** tcd compares the cutoff against what is expected for that
bitrate:
| Stated bitrate | Expected cutoff ratio |
|------------------|----------------------|
| < 192 kbps | ≥ 0.75 of Nyquist |
| 192–255 kbps | ≥ 0.85 of Nyquist |
| ≥ 256 kbps | ≥ 0.90 of Nyquist |
| Stated bitrate | Expected cutoff |
|----------------|-----------------|
| <192 kbps | ≥75% of Nyquist |
| 192–255 kbps | ≥85% of Nyquist |
| ≥256 kbps | ≥90% of Nyquist |
If the measured cutoff is **more than 8 percentage points below** the expected
minimum, the file is classified as **UPSCALED** (a lower-bitrate encode that
was decoded and re-encoded at a higher bitrate). Otherwise it is **NATIVE**
(a single, genuine encode at the stated bitrate).
If the actual cutoff is **more than 8 percentage points lower** than expected,
the file is **UPSCALED**. For example, a file claiming 320 kbps (expecting
≥90%) but showing a cutoff of 70% (≈15400 Hz) would be flagged as upscaled
from ~96 kbps.
### B. Input is a lossless codec (flac, pcm, alac, wavpack, …)
Otherwise it is **NATIVE** — a genuine single encode at this bitrate.
The tool applies two layers of criteria.
### Scenario 2: The input file is lossless (FLAC, WAV, ALAC, etc.)
#### Primary criteria (cutoff + transition bandwidth)
The file claims to be lossless. The question is: was it actually created by
decoding a lossy file and re-encoding to lossless?
The transition bandwidth (from −20 dB to −60 dB) is compared against a
cutoff-dependent threshold. A narrower bandwidth than the threshold indicates
a lossy encoder's brickwall filter:
tcd uses a **two-layer** check:
| Cutoff ratio range | Max transition bandwidth | Interpretation |
|-------------------|-------------------------|---------------|
| < 0.50 | 4000 Hz | Transcode |
| < 0.70 | 3000 Hz | Transcode |
| < 0.80 | 2000 Hz | Transcode |
| < 0.90 | 1200 Hz | Transcode |
| ≥ 0.90 | 500 Hz | Transcode |
**Layer 1 — Cutoff + Steepness (primary):**
This graduated approach avoids the earlier problem of rigid breakpoints that
could miss files with moderate cutoffs but wider-than-expected transition
bands, or files with cutoffs just above a hard threshold (e.g. 21 kHz /
44.1 kHz = 0.952, previously missed by a strict `< 0.95` check).
| Cutoff range | Max steepness allowed | If exceeded → |
|-------------|----------------------|---------------|
| <50% of Nyquist | 4000 Hz | TRANSCODE |
| 50–70% | 3000 Hz | TRANSCODE |
| 70–80% | 2000 Hz | TRANSCODE |
| 80–90% | 1200 Hz | TRANSCODE |
| ≥90% | 500 Hz | TRANSCODE |
The combination of a low cutoff and a sharp roll-off is the strongest
indicator. A cutoff below 50 % of Nyquist (e.g. 11 kHz at 44.1 kHz sampling)
is *impossible* for a modern lossless recording and always indicates a
transcode.
This works because lossy cutoffs are always sharp (low steepness). A lossless
recording that happens to have a low cutoff (e.g., a muddy recording with
little treble) would still have a *gradual* roll-off (high steepness) — you
need both a low cutoff **and** a sharp drop to convict.
#### Secondary criteria (roughness + band ratio)
**Layer 2 — Roughness + Band ratio (secondary):**
If the primary criteria do not match but the cutoff is above 80 % of Nyquist
(the region where lossy cutoffs can approach the lossless range), the tool
falls back to roughness and band ratio:
If Layer 1 did not trigger but the cutoff is above 80%, tcd checks roughness
and band ratio. This catches transcodes where the cutoff happens to be high
enough to pass Layer 1 but the spectrum is still bumpy and depleted in the
top band:
| Roughness | Band ratio | Interpretation |
|-----------|----------------|----------------|
| > 0.40 | any | Transcode |
| > 0.30 | < 0.90 | Transcode |
| > 0.20 | < 0.85 | Transcode |
| Roughness | Band ratio | If matched → |
|-----------|------------|--------------|
| >0.40 | any | TRANSCODE |
| >0.30 | <0.90 | TRANSCODE |
| >0.20 | <0.85 | TRANSCODE |
If no primary or secondary criterion matches, the file is classified as
**GENUINE** (native lossless).
If neither layer triggers, the file is **GENUINE** (native lossless).
### C. Confidence score
---
### The verdicts at a glance
| Verdict | Input codec | What it means |
|---------|------------|---------------|
| **NATIVE** | lossy | Encoded once at the stated bitrate — genuine |
| **UPSCALED** | lossy | Originally encoded at a lower bitrate, then re-encoded higher |
| **GENUINE** | lossless | Appears to be native lossless — no evidence of lossy origin |
| **TRANSCODE** | lossless | Originated from a lossy source, decoded to lossless |
| **SILENT** | any | No detectable audio content |
---
Confidence score
----------------
A continuous **confidence** (0–100 %) is computed using the same metrics with
a sliding scale, providing a graded measure of how certain the tool is about
its verdict.
### D. Auto-remove mode (`-a`)
---
Auto-remove mode (`-a`)
------------------------
When `-a` is passed, any file that is not classified as NATIVE or GENUINE is
automatically deleted after analysis. This is useful for batch cleanup of
corrupt or transcoded libraries. Careful - that this will eat data.
corrupt or transcoded libraries. Careful — this will eat data.
---
Why the method is (somewhat!) scientifically reliable
------------------------------------------
------------------------------------------------------
### 1. Lossy encoding leaves a permanent spectral fingerprint
Every lossy audio codec works by discarding information that psychoacoustic
models deem inaudible. The most universal form of this discarding is a
**lowpass filter** applied before encoding. Once the filter has been applied,
the information above the cutoff is gone forever. Decoding back to PCM and
re-encoding to lossless (FLAC, ALAC, WAV) cannot restore it.
Every lossy audio codec discards information. The most obvious form is a
**lowpass filter** — once applied, the frequencies above the cutoff are gone
forever. Decoding back to PCM and re-encoding to lossless cannot restore them.
This means a "lossless" FLAC file made from an MP3 will contain the MP3's
permanent spectral cutoff.
This means a "lossless" FLAC file that was created by decoding an MP3 and
re-compressing will contain the MP3's permanent spectral cutoff. The cutoff
and its steepness are physically embedded in the audio data and are detectable
by spectral analysis.
### 2. Steepness catches the filter shape
Multiple independent studies in the audio forensics community have confirmed
that frequency-domain analysis of cutoffs is a reliable method for identifying
lossy-sourced lossless files (see e.g. the work on "MP3Cut" and similar
tools).
Lossy encoders use sharp digital filters (brick-wall style) that drop from
audible to silent in a few hundred Hz. Natural acoustic sources (voice,
instruments, room ambience) roll off gradually over many kHz. A drop steeper
than 2 kHz at any cutoff position is extremely unlikely to occur naturally.
### 2. The steepness metric catches the filter topology
- **MP3 (ISO/IEC 11172-3):** typical roll-off of several hundred Hz to ~2 kHz
- **AAC (ISO/IEC 13818-7):** sharper, often 200–800 Hz
- **Vorbis:** variable but always steeper than natural
Lossy encoders use FIR or hybrid filterbanks with a characteristic roll-off
slope. The steepness measurement directly captures the *order* and *design*
of that filter:
### 3. Roughness detects double-encoding noise
- **MP3 (ISO/IEC 11172-3)** uses a hybrid polyphase/MDCT filterbank with a
typical roll-off of several hundred Hz to about 2 kHz, depending on the
bitrate and encoder implementation (LAME, Fraunhofer, etc.).
- **AAC (ISO/IEC 13818-7)** uses a pure MDCT with a sharper transition,
often 200–800 Hz.
- **Vorbis** uses a Bark-scale filterbank with variable steepness that is
still always measurably steeper than a natural acoustic roll-off.
Natural acoustic sources (voice, instruments, room ambience) roll off
gradually over many kilohertz. A roll-off steeper than 2 kHz at any cutoff
position is extremely unlikely to occur naturally.
### 3. Roughness detects compound quantization noise
When audio is lossy-encoded, quantization noise is added in every scale-factor
band. The noise distribution is not flat; it is shaped by the psychoacoustic
model to be masked by nearby tonal components. When the audio is decoded and
re-encoded, a *second* layer of noise-shaping is applied, creating
When audio is lossy-encoded, quantization noise is shaped to be masked by the
music. Re-encoding adds a *second* layer of noise-shaping, creating
irregularities in the spectrum that are statistically unlikely in a single
encode.
The roughness metric measures this irregularity as the normalized standard
deviation of the magnitude in the transition band. Values above 0.20–0.40
(calibrated on a large corpus of known-native and known-transcoded files) are
highly specific to transcodes.
### 4. Band ratio exploits the Fletcher–Munson curves
Human hearing is least sensitive above 16 kHz. Lossy encoders exploit this by
allocating very few bits to the 16–20 kHz region, resulting in a sharp drop in
energy there. The band ratio metric captures this drop. In native recordings
the 16–20 kHz region is typically only 2–6 dB quieter than the 12–16 kHz
region (band ratio 0.5–1.0). In transcoded material it is often 10–20 dB
quieter (band ratio < 0.3).
spending almost no bits there. In native recordings, the 16–20 kHz region is
typically only 2–6 dB quieter than 12–16 kHz (band ratio 0.5–1.0). In
transcoded material it is often 10–20 dB quieter (band ratio < 0.3).
### 5. Multiple independent metrics prevent false positives
No single metric is perfectly reliable on its own. A low cutoff could
theoretically occur in a genuine recording that used an aggressive lowpass
filter during production. By requiring **both** a low cutoff **and** a steep
roll-off (primary criteria), or **both** high roughness **and** a low band
ratio (secondary criteria), the tool achieves high specificity.
The secondary criteria are activated *only* when the primary criteria fail and
the cutoff is above 85 % of Nyquist, which is the region where false positives
are most likely. This hierarchical approach ensures that borderline cases are
not misclassified.
No single metric is perfectly reliable. A low cutoff could occur in a genuine
recording that used an aggressive lowpass during mastering. By requiring
**both** a low cutoff **and** a sharp roll-off (primary), or **both** high
roughness **and** a low band ratio (secondary), the tool avoids false
positives.
### 6. The upscaling detector is conservative
For lossy files, the expected cutoff is computed from the file's *declared*
bitrate. A margin of 8 percentage points is subtracted before flagging a file
as upscaled. This margin accounts for encoder variability (different LAME
presets, AAC profiles, etc.) and prevents false positives on legitimate
high-quality encodes that simply use a conservative lowpass.
For lossy files, an 8-percentage-point margin is subtracted before flagging a
file as upscaled. This accounts for encoder variability and prevents false
positives on legitimate encodes that simply use a conservative lowpass.
---
@ -352,4 +400,3 @@ References
1999 (Fletcher–Munson equal-loudness contours).
- Lerch, A. - *An Introduction to Audio Content Analysis*, Wiley, 2012
(spectral features for audio forensics).

22
tcd.c
View file

@ -616,15 +616,19 @@ static void render_visual_spectrum(
char title[256];
const char *short_name = strrchr(filename, '/');
short_name = short_name ? short_name + 1 : filename;
int info_len = snprintf(title, sizeof(title), " %s | %s | %d Hz | %d ch",
short_name, fmt_name, sample_rate, channels);
int info_len = snprintf(title, sizeof(title), " %s | %s | %d Hz | %d ch | %lldk",
short_name, fmt_name, sample_rate, channels, (long long)(bitrate / 1000));
int total_w = term_w;
if (info_len > total_w) {
char truncated[256];
snprintf(truncated, sizeof(truncated), " %s | %d Hz | %d ch",
short_name, sample_rate, channels);
snprintf(truncated, sizeof(truncated), " %s | %d Hz | %d ch | %lldk",
short_name, sample_rate, channels, (long long)(bitrate / 1000));
if ((int)strlen(truncated) > total_w) {
snprintf(truncated, sizeof(truncated), " %s | %d ch", short_name, channels);
snprintf(truncated, sizeof(truncated), " %s | %d ch | %lldk",
short_name, channels, (long long)(bitrate / 1000));
if ((int)strlen(truncated) > total_w) {
snprintf(truncated, sizeof(truncated), " %s | %lldk",
short_name, (long long)(bitrate / 1000));
if ((int)strlen(truncated) > total_w) {
snprintf(truncated, sizeof(truncated), " %s", short_name);
if ((int)strlen(truncated) > total_w) {
@ -632,6 +636,7 @@ static void render_visual_spectrum(
}
}
}
}
printf(ANSI_BOLD ANSI_CYAN "%s" ANSI_RESET, truncated);
printf("\n");
} else {
@ -1294,7 +1299,7 @@ static int process_file(const char *filename, const Options *opts)
printf(ANSI_BOLD ANSI_CYAN "Channels:" ANSI_RESET " %d\n", channels);
printf(ANSI_BOLD ANSI_CYAN "Windows:" ANSI_RESET " %d\n", analyzer.count);
printf(ANSI_BOLD ANSI_CYAN "Peak:" ANSI_RESET " %.1f dBFS\n", peak_db);
printf(ANSI_BOLD ANSI_CYAN "Cutoff:" ANSI_RESET " %.0f Hz (%.1f%% of Nyquist)\n", cutoff_hz, 100.0 * cutoff_hz / nyquist);
printf(ANSI_BOLD ANSI_CYAN "Cutoff:" ANSI_RESET " %.0f / %.0f Hz = %.1f%%\n", cutoff_hz, nyquist, 100.0 * cutoff_hz / nyquist);
printf(ANSI_BOLD ANSI_CYAN "Steepness:" ANSI_RESET " %.0f Hz\n", steepness);
printf(ANSI_BOLD ANSI_CYAN "Roughness:" ANSI_RESET " %.3f\n", roughness);
printf(ANSI_BOLD ANSI_CYAN "Band ratio:" ANSI_RESET " %.3f\n", band_ratio);
@ -1329,8 +1334,9 @@ static int process_file(const char *filename, const Options *opts)
} else {
printf(ANSI_GREEN "NATIVE" ANSI_RESET " (single encode at this bitrate)\n");
}
printf(ANSI_BOLD ANSI_CYAN "Verdict Info:" ANSI_RESET " cutoff=%.1f%% Nyquist, expected≥%.0f%% for %lld kbps",
100.0 * cutoff_ratio, 100.0 * expected_min,
printf(ANSI_BOLD ANSI_CYAN "Verdict Info:" ANSI_RESET " bandwidth used=%.1f%% (%.0f/%.0f Hz), expected≥%.0f%% for %lld kbps",
100.0 * cutoff_ratio, effective_cutoff, nyquist,
100.0 * expected_min,
bitrate > 0 ? (long long)(bitrate / 1000) : 0);
if (upscaled) {
printf(" → suggests ");