diff --git a/README.md b/README.md
index 860be2d87a20516889eb8c5bf0aeb38090e6089e..89ec0309aef0379be0ef54d425ca83fc031274b8 100644
--- a/README.md
+++ b/README.md
@@ -8,17 +8,26 @@ the GSPR loss function with a second-order polynomial.
Prerequisite
------------
-This program uses the following libraries (other versions might work):
+This program uses the following libraries (tested with the following version):
+* cython 0.29
+* numpy 1.21
+* scipy 1.7.1
+* soundfile 0.9
+* scikit-learn 1.0 (needed if you use the hyper resolution mode)
+* pandas 1.3 (needed only for saving in a non CSV format)
+* tqdm 4.62 (optional if you want a progress bar)
Usage
-----
-
+```
usage: gsrp_tdoa_hyperres.py [-h] [-c CHANNELS] [-i INVERSE] [-f FRAME_SIZE]
- [-s HOP_SIZE] [-m MAX_TDOA] [-l LOW] [-u UP]
- [-d DECIMATE] [-t] [-e] [--start SECONDS]
- [--end SECONDS]
+ [-s STRIDE | -p STRIDE] [-m MAX_TDOA]
+ [-S SECONDS] [-E SECONDS] [-l LOW] [-u UP]
+ [-d DECIMATE] [-t] [-e] [-n] [-w]
+ [-M {prepare,on-the-fly,auto,smart}] [-q QUOTA]
+ [-v]
infile outfile
-
+```
Arguments
-----
@@ -29,10 +38,9 @@ positional arguments:
infile The sound file to process.
outfile The text or npy file to write results to. Each row
gives the position (in samples), cross-correlation
- product, the independent TDOAs (in samples), and TDOAs
- derived from the independent ones. For plots, the
- panes give the cross-correlation product, independent
- TDOAS and derived TDOAs from top to bottom.
+ product in decibel (normalized and unormalized), the
+ independent TDOAs (in samples), and TDOAs derived from
+ the independent ones.
optional arguments:
-h, --help show this help message and exit
@@ -51,11 +59,13 @@ Channels:
Size settings:
-f FRAME_SIZE, --frame-size FRAME_SIZE
The size of the cross-correlation frames in seconds
- (default: 0.2)
+ (default: 0.02)
-s STRIDE, --stride STRIDE
The step between the beginnings of sequential frames
- in seconds (default: 0.01), or the postion in second
- if csv file path is given.
+ in seconds (default: 0.01)
+ -p STRIDE, --pos STRIDE
+ The position in second from csv file path. Not allowed
+ if stride is set
-m MAX_TDOA, --max-tdoa MAX_TDOA
The maximum TDOA in seconds (default: 0.0011).
-S SECONDS, --start SECONDS
@@ -76,18 +86,27 @@ Other:
-e, --erase Erase existing outfile. If outfile exist and --erase
is not provide, the script will exit.
-n, --no-hyperres Disable the hyper resolution evalutation of the TDOA
- -M {smart,auto,on-the-fly,prepare}, --mode {smart,auto,on-the-fly,prepare}
- How to explore the TDOA space (default: prepare).
- 'prepare' precomputes all the possible TDOA
+ -w, --wide Use only one level to concatenate the normal and
+ hyperres results. Behaviour depends on the output file
+ type.
+ -M {prepare,on-the-fly,auto,smart}, --mode {prepare,on-the-fly,auto,smart}
+ How to explore the TDOA space (default: smart).
+ prepare precomputes all the possible TDOA
pairs and then evaluate them. All the results are save
in memory.
- 'on-the-fly' compute the TDOA pairs at the same
+ on-the-fly compute the TDOA pairs at the same
time as it compute the loss function. Only the maximum
- is saved. Can be slower than 'prepare'.
- 'smart' gradually increase the search space dimension, '
- reducing the number of tdoa to evaluate.
- 'auto' automatically try to pick the right
+ is saved. Can be slower than prepare.
+ smart gradually increase the search space
+ dimension, reducing the number of tdoa to evaluate.
+ auto automatically try to pick the right
method.
-
-Process finished with exit code 0
+ -q QUOTA, --quota QUOTA
+ Memory limit in bytes for the
+ {BColors.BOLD}smart{BColors.ENDC} method. If hit, halt
+ the computation of the current frame and skip to the
+ next one. Note that it does not account for other
+ memory usage, such as the sound data. Can be a unit
+ such as GB, GiO, Ko, ...
+ -v, --verbose Activate verbose for smart mode
```
diff --git a/gsrp_tdoa_hyperres.py b/gsrp_tdoa_hyperres.py
index 3622fd0bc9c0bdd08f26baa5cbfb2daf755fc346..345873c47e8261360e9abf35e6a02db82e6305c6 100755
--- a/gsrp_tdoa_hyperres.py
+++ b/gsrp_tdoa_hyperres.py
@@ -8,9 +8,6 @@ import scipy.signal as sg
import soundfile as sf
from numpy.fft import rfft, irfft
from scipy.signal.windows import tukey
-from sklearn.linear_model import LinearRegression
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import PolynomialFeatures
from gsrp_smart_util import *
@@ -104,6 +101,10 @@ def corr(data, pos, w_size, max_tdoa, decimate=1, mode='prepare', hyper=True, ve
tdoas = np.zeros((len(pos), num_channel_pairs + 2), np.float32)
if hyper: # prepare hyper res
+ from sklearn.linear_model import LinearRegression
+ from sklearn.pipeline import Pipeline
+ from sklearn.preprocessing import PolynomialFeatures
+
tdoas2 = np.zeros((len(pos), num_channel_pairs + 2), np.float32)
poly = PolynomialFeatures(2)
lin = LinearRegression()