diff --git a/code/scipy/signal/signal.c b/code/scipy/signal/signal.c
index f930a943..53554e2d 100644
--- a/code/scipy/signal/signal.c
+++ b/code/scipy/signal/signal.c
@@ -10,6 +10,7 @@
  *               2020 Scott Shawcroft for Adafruit Industries
  *               2020-2021 Zoltán Vörös
  *               2020 Taku Fukada
+ *               2024 Edouard Leroy (oaconvolve implementation)
 */
 
 #include <math.h>
@@ -19,6 +20,8 @@
 #include "../../ulab.h"
 #include "../../ndarray.h"
 #include "../../numpy/carray/carray_tools.h"
+#include "../../numpy/fft/fft_tools.h"
+#include "../../ulab_tools.h"
 
 #if ULAB_SCIPY_SIGNAL_HAS_SOSFILT & ULAB_MAX_DIMS > 1
 static void signal_sosfilt_array(mp_float_t *x, const mp_float_t *coeffs, mp_float_t *zf, const size_t len) {
@@ -120,11 +123,157 @@ mp_obj_t signal_sosfilt(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_ar
 MP_DEFINE_CONST_FUN_OBJ_KW(signal_sosfilt_obj, 2, signal_sosfilt);
 #endif /* ULAB_SCIPY_SIGNAL_HAS_SOSFILT */
 
+#if ULAB_SCIPY_SIGNAL_HAS_OACONVOLVE
+
+mp_obj_t signal_oaconvolve(size_t n_args, const mp_obj_t *pos_args, mp_map_t *kw_args) {
+    static const mp_arg_t allowed_args[] = {
+        {MP_QSTR_a, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE}},
+        {MP_QSTR_v, MP_ARG_REQUIRED | MP_ARG_OBJ, {.u_rom_obj = MP_ROM_NONE}},
+    };
+
+    mp_arg_val_t args[MP_ARRAY_SIZE(allowed_args)];
+    mp_arg_parse_all(n_args, pos_args, kw_args, MP_ARRAY_SIZE(allowed_args), allowed_args, args);
+
+    if (!mp_obj_is_type(args[0].u_obj, &ulab_ndarray_type) || !mp_obj_is_type(args[1].u_obj, &ulab_ndarray_type)) {
+        mp_raise_TypeError(MP_ERROR_TEXT("oa convolve arguments must be ndarrays"));
+    }
+
+    ndarray_obj_t *a = MP_OBJ_TO_PTR(args[0].u_obj);
+    ndarray_obj_t *c = MP_OBJ_TO_PTR(args[1].u_obj);
+// deal with linear arrays only
+#if ULAB_MAX_DIMS > 1
+    if ((a->ndim != 1) || (c->ndim != 1)) {
+        mp_raise_TypeError(MP_ERROR_TEXT("convolve arguments must be linear arrays"));
+    }
+#endif
+    size_t signal_len = a->len;
+    size_t kernel_len = c->len;
+    if (signal_len == 0 || kernel_len == 0) {
+        mp_raise_TypeError(MP_ERROR_TEXT("convolve arguments must not be empty"));
+    }
+
+    
+
+    size_t segment_len = kernel_len; // Min segment size, at least size of kernel
+    size_t fft_size = 1;
+    while (fft_size < segment_len + kernel_len - 1) {
+        fft_size *= 2; // Find smallest power of 2 for fft size
+    }
+    segment_len = fft_size - kernel_len + 1; // Final segment size
+    size_t output_len = signal_len + kernel_len - 1;
+    uint8_t dtype = NDARRAY_FLOAT;
+#if ULAB_SUPPORTS_COMPLEX
+    if ((a->dtype == NDARRAY_COMPLEX) || (c->dtype == NDARRAY_COMPLEX)) {
+        dtype = NDARRAY_COMPLEX;
+    }
+#endif
+
+    uint8_t sz = 2 * sizeof(mp_float_t);
+
+    // Buffer allocation
+    mp_float_t *kernel_fft_array = m_new0(mp_float_t, fft_size * 2);
+    mp_float_t *segment_fft_array = m_new0(mp_float_t, fft_size * 2);
+    ndarray_obj_t *output = ndarray_new_linear_array(output_len, dtype);
+    mp_float_t *output_array = (mp_float_t *)output->array;
+    uint8_t *kernel_array = (uint8_t *)c->array;
+    uint8_t *signal_array = (uint8_t *)a->array;
+
+    // Copy kernel data
+    if (c->dtype == NDARRAY_COMPLEX) {
+        for (size_t i = 0; i < kernel_len; i++){
+            memcpy(kernel_fft_array, kernel_array, sz);
+            kernel_fft_array += 2;
+            kernel_array += c->strides[ULAB_MAX_DIMS - 1];
+        }
+    }
+    else {
+        mp_float_t (*func)(void *) = ndarray_get_float_function(c->dtype);
+        for (size_t i = 0; i < kernel_len; i++) {
+            // real part; the imaginary part is 0, no need to assign
+            *kernel_fft_array = func(kernel_array);
+            kernel_fft_array += 2;
+            kernel_array += c->strides[ULAB_MAX_DIMS - 1];
+        }
+    }
+    kernel_fft_array -= 2 * kernel_len;
+
+    // Compute kernel fft in place
+    fft_kernel(kernel_fft_array, fft_size, 1);
+
+    mp_float_t real, imag; // For complex multiplication
+    size_t current_segment_size = segment_len;
+    mp_float_t (*funca)(void *) = ndarray_get_float_function(a->dtype);
+
+    for (size_t i = 0; i < signal_len; i += segment_len) {
+        // Check if remaining data is less than segment length
+        if (i + segment_len > signal_len) {
+            current_segment_size = signal_len - i;
+        }
+        // Load segment in buffer
+        memset(segment_fft_array, 0, fft_size * sz); // Reset buffer to zero
+        if (a->dtype == NDARRAY_COMPLEX) {
+            for (size_t k = 0; k < current_segment_size; k++){
+                memcpy(segment_fft_array, signal_array, sz);
+                segment_fft_array += 2;
+                signal_array += a->strides[ULAB_MAX_DIMS - 1];
+            }
+        }
+        else {
+            for (size_t k = 0; k < current_segment_size; k++) {
+                // real part; the imaginary part is 0, no need to assign
+                *segment_fft_array = funca(signal_array);
+                segment_fft_array += 2;
+                signal_array += a->strides[ULAB_MAX_DIMS - 1];
+            }
+        }
+        segment_fft_array -= 2 * current_segment_size;
+
+        // Compute segment fft in place
+        fft_kernel(segment_fft_array, fft_size, 1);
+
+        // Product of kernel and segment fft (complex multiplication)
+        for (size_t j = 0; j < fft_size; j++) {
+            real = segment_fft_array[j * 2] * kernel_fft_array[j * 2] - segment_fft_array[j * 2 + 1] * kernel_fft_array[j * 2 + 1];
+            imag = segment_fft_array[j * 2] * kernel_fft_array[j * 2 + 1] + segment_fft_array[j * 2 + 1] * kernel_fft_array[j * 2];
+            segment_fft_array[j * 2] = real;
+            segment_fft_array[j * 2 + 1] = imag;
+        }
+        // Inverse FFT in place
+        fft_kernel(segment_fft_array, fft_size, -1);
+
+        #if ULAB_SUPPORTS_COMPLEX
+        if (dtype == NDARRAY_COMPLEX) {
+            // Overlap, Add and normalized inverse fft
+            for (size_t j = 0; j < fft_size * 2; j++) {
+                if ((i * 2 + j) < (output_len * 2)) {
+                    output_array[i * 2 + j] += (segment_fft_array[j] / fft_size);
+                }
+            }
+            continue;
+        }
+        #endif
+        
+        
+        for (size_t j = 0; j < fft_size; j++) {
+            if ((i + j) < (output_len)) { // adds only real part
+                output_array[i + j] += (segment_fft_array[j * 2] / fft_size);
+            }
+        }   
+    }
+    return MP_OBJ_FROM_PTR(output);
+}
+MP_DEFINE_CONST_FUN_OBJ_KW(signal_oaconvolve_obj, 2, signal_oaconvolve);
+#endif /* ULAB_SCIPY_SIGNAL_HAS_OACONVOLVE */
+
+
 static const mp_rom_map_elem_t ulab_scipy_signal_globals_table[] = {
     { MP_ROM_QSTR(MP_QSTR___name__), MP_ROM_QSTR(MP_QSTR_signal) },
     #if ULAB_SCIPY_SIGNAL_HAS_SOSFILT & ULAB_MAX_DIMS > 1
         { MP_ROM_QSTR(MP_QSTR_sosfilt), MP_ROM_PTR(&signal_sosfilt_obj) },
     #endif
+    #if ULAB_SCIPY_SIGNAL_HAS_OACONVOLVE
+        { MP_ROM_QSTR(MP_QSTR_oaconvolve), MP_ROM_PTR(&signal_oaconvolve_obj) },
+    #endif
 };
 
 static MP_DEFINE_CONST_DICT(mp_module_ulab_scipy_signal_globals, ulab_scipy_signal_globals_table);
diff --git a/code/scipy/signal/signal.h b/code/scipy/signal/signal.h
index 033f6e4c..3b713187 100644
--- a/code/scipy/signal/signal.h
+++ b/code/scipy/signal/signal.h
@@ -19,5 +19,6 @@
 extern const mp_obj_module_t ulab_scipy_signal_module;
 
 MP_DECLARE_CONST_FUN_OBJ_KW(signal_sosfilt_obj);
+MP_DECLARE_CONST_FUN_OBJ_KW(signal_oaconvolve_obj);
 
 #endif /* _SCIPY_SIGNAL_ */
diff --git a/code/ulab.c b/code/ulab.c
index 12f37027..17058454 100644
--- a/code/ulab.c
+++ b/code/ulab.c
@@ -33,7 +33,7 @@
 #include "user/user.h"
 #include "utils/utils.h"
 
-#define ULAB_VERSION 6.6.1
+#define ULAB_VERSION 6.6.2
 #define xstr(s) str(s)
 #define str(s) #s
 
diff --git a/code/ulab.h b/code/ulab.h
index 78b8a1ba..30c237b5 100644
--- a/code/ulab.h
+++ b/code/ulab.h
@@ -740,6 +740,10 @@
 #define ULAB_SCIPY_SIGNAL_HAS_SOSFILT       (1)
 #endif
 
+#ifndef ULAB_SCIPY_SIGNAL_HAS_OACONVOLVE
+#define ULAB_SCIPY_SIGNAL_HAS_OACONVOLVE       (1)
+#endif
+
 #ifndef ULAB_SCIPY_HAS_OPTIMIZE_MODULE
 #define ULAB_SCIPY_HAS_OPTIMIZE_MODULE      (1)
 #endif
diff --git a/docs/manual/source/scipy-signal.rst b/docs/manual/source/scipy-signal.rst
index d1f34818..0f078eba 100644
--- a/docs/manual/source/scipy-signal.rst
+++ b/docs/manual/source/scipy-signal.rst
@@ -2,9 +2,10 @@
 scipy.signal
 ============
 
-This module defines the single function:
+This module defines the functions:
 
 1. `scipy.signal.sosfilt <#sosfilt>`__
+2. `scipy.signal.oaconvolve <#oaconvolve>`__
 
 sosfilt
 -------
@@ -64,6 +65,39 @@ initial values are assumed to be 0.
     ========================================
     zf:  array([[37242.0, 74835.0],
     	 [1026187.0, 1936542.0]], dtype=float)
+
+oaconvolve
+-------
+
+``scipy``:
+https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.oaconvolve.html
+
+Convolve two N-dimensional arrays using the overlap-add method.
+
+Convolve in1 and in2 using the overlap-add method. Similarly to numpy.convolve, 
+this method works in full mode and other modes can be obtained by slicing the result?
+
+This is generally much faster than linear convolve for large arrays (n > ~500), 
+and generally much faster than fftconvolve (not implemented yet in ulab) when one array is much larger 
+than the other (e.g. for a matched filter algorithm), but can be slower when only a few output values 
+are needed or when the arrays are very similar in shape, and can only output float arrays (int or object array inputs will be cast to float).
+
+.. code::
+        
+    # code to be run in micropython
+    
+    from ulab import numpy as np
+    from ulab import scipy as spy
+    
+    x = np.array((1,2,3))
+    y = np.array((1,10,100,1000))
+    result = spy.signal.oaconvolve(x, y)
+    print('result: ', result)
+
+.. parsed-literal::
+
+    result:  array([1.0, 12.00024, 123.0001, 1230.0, 2300.0, 3000.0], dtype=float32)
+    
     
     
 
diff --git a/docs/scipy-signal.ipynb b/docs/scipy-signal.ipynb
index ec10d2e6..8abfcde5 100644
--- a/docs/scipy-signal.ipynb
+++ b/docs/scipy-signal.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-01-12T16:11:12.111639Z",
@@ -11,10 +11,19 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'matplotlib'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpylab\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\EL221079\\Documents\\MICROPYTHON\\oaconvolve\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:2480\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[1;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[0;32m   2478\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[0;32m   2479\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[1;32m-> 2480\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2482\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[0;32m   2483\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[0;32m   2484\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[0;32m   2485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
+      "File \u001b[1;32mc:\\Users\\EL221079\\Documents\\MICROPYTHON\\oaconvolve\\.venv\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:159\u001b[0m, in \u001b[0;36mPylabMagics.pylab\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m    155\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    156\u001b[0m     \u001b[38;5;66;03m# invert no-import flag\u001b[39;00m\n\u001b[0;32m    157\u001b[0m     import_all \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m args\u001b[38;5;241m.\u001b[39mno_import_all\n\u001b[1;32m--> 159\u001b[0m gui, backend, clobbered \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_pylab\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimport_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimport_all\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n\u001b[0;32m    161\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m    162\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mpylab is deprecated, use \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mmatplotlib inline and import the required libraries.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    163\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\EL221079\\Documents\\MICROPYTHON\\oaconvolve\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3719\u001b[0m, in \u001b[0;36mInteractiveShell.enable_pylab\u001b[1;34m(self, gui, import_all, welcome_message)\u001b[0m\n\u001b[0;32m   3692\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Activate pylab support at runtime.\u001b[39;00m\n\u001b[0;32m   3693\u001b[0m \n\u001b[0;32m   3694\u001b[0m \u001b[38;5;124;03mThis turns on support for matplotlib, preloads into the interactive\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3715\u001b[0m \u001b[38;5;124;03m    This argument is ignored, no welcome message will be displayed.\u001b[39;00m\n\u001b[0;32m   3716\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3717\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpylabtools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m import_pylab\n\u001b[1;32m-> 3719\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3721\u001b[0m \u001b[38;5;66;03m# We want to prevent the loading of pylab to pollute the user's\u001b[39;00m\n\u001b[0;32m   3722\u001b[0m \u001b[38;5;66;03m# namespace as shown by the %who* magics, so we execute the activation\u001b[39;00m\n\u001b[0;32m   3723\u001b[0m \u001b[38;5;66;03m# code in an empty namespace, and we update *both* user_ns and\u001b[39;00m\n\u001b[0;32m   3724\u001b[0m \u001b[38;5;66;03m# user_ns_hidden with this information.\u001b[39;00m\n\u001b[0;32m   3725\u001b[0m ns \u001b[38;5;241m=\u001b[39m {}\n",
+      "File \u001b[1;32mc:\\Users\\EL221079\\Documents\\MICROPYTHON\\oaconvolve\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3665\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[1;34m(self, gui)\u001b[0m\n\u001b[0;32m   3662\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m\n\u001b[0;32m   3664\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m-> 3665\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[43mpt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_gui_and_backend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpylab_gui_select\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3667\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gui \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   3668\u001b[0m     \u001b[38;5;66;03m# If we have our first gui selection, store it\u001b[39;00m\n\u001b[0;32m   3669\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\EL221079\\Documents\\MICROPYTHON\\oaconvolve\\.venv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:338\u001b[0m, in \u001b[0;36mfind_gui_and_backend\u001b[1;34m(gui, gui_select)\u001b[0m\n\u001b[0;32m    321\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfind_gui_and_backend\u001b[39m(gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, gui_select\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    322\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Given a gui string return the gui and mpl backend.\u001b[39;00m\n\u001b[0;32m    323\u001b[0m \n\u001b[0;32m    324\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[38;5;124;03m    'WXAgg','Qt4Agg','module://matplotlib_inline.backend_inline','agg').\u001b[39;00m\n\u001b[0;32m    336\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 338\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m    340\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m _matplotlib_manages_backends():\n\u001b[0;32m    341\u001b[0m         backend_registry \u001b[38;5;241m=\u001b[39m matplotlib\u001b[38;5;241m.\u001b[39mbackends\u001b[38;5;241m.\u001b[39mregistry\u001b[38;5;241m.\u001b[39mbackend_registry\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
      ]
     }
    ],
@@ -22,6 +31,11 @@
     "%pylab inline"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -31,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-01-29T20:50:20.813162Z",
@@ -49,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2022-01-29T20:50:21.613220Z",
@@ -131,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-05-07T07:35:35.126401Z",
@@ -147,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-05-19T19:11:18.145548Z",
@@ -162,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-05-07T07:35:38.725924Z",
@@ -225,9 +239,10 @@
    "source": [
     "# scipy.signal\n",
     "\n",
-    "This module defines the single function:\n",
+    "This module defines the functions:\n",
     "\n",
-    "1. [scipy.signal.sosfilt](#sosfilt)"
+    "1. [scipy.signal.sosfilt](#sosfilt)\n",
+    "2. [scipy.signal.oaconvolve](#oaconvolve)"
    ]
   },
   {
@@ -245,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-06-19T20:24:10.529668Z",
@@ -277,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-06-19T20:27:39.508508Z",
@@ -314,11 +329,60 @@
     "print('y: ', y)\n",
     "print('\\n' + '='*40 + '\\nzf: ', zf)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## oaconvolve\n",
+    "\n",
+    "``scipy``:\n",
+    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.oaconvolve.html\n",
+    "\n",
+    "Convolve two N-dimensional arrays using the overlap-add method.\n",
+    "\n",
+    "Convolve in1 and in2 using the overlap-add method. Similarly to numpy.convolve, \n",
+    "this method works in full mode and other modes can be obtained by slicing the result?\n",
+    "\n",
+    "This is generally much faster than linear convolve for large arrays (n > ~500), \n",
+    "and generally much faster than fftconvolve (not implemented yet in ulab) when one array is much larger \n",
+    "than the other (e.g. for a matched filter algorithm), but can be slower when only a few output values \n",
+    "are needed or when the arrays are very similar in shape, and can only output float arrays (int or object array inputs will be cast to float).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'ulab'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# code to be run in micropython\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mulab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mulab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m scipy \u001b[38;5;28;01mas\u001b[39;00m spy\n\u001b[0;32m      6\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray((\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m))\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ulab'"
+     ]
+    }
+   ],
+   "source": [
+    " # code to be run in micropython\n",
+    "    \n",
+    "from ulab import numpy as np\n",
+    "from ulab import scipy as spy\n",
+    "\n",
+    "x = np.array((1,2,3))\n",
+    "y = np.array((1,10,100,1000))\n",
+    "result = spy.signal.oaconvolve(x, y)\n",
+    "print('result: ', result)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -332,7 +396,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tests/1d/scipy/oaconvolve.py b/tests/1d/scipy/oaconvolve.py
new file mode 100644
index 00000000..48e8492a
--- /dev/null
+++ b/tests/1d/scipy/oaconvolve.py
@@ -0,0 +1,16 @@
+import math
+
+try:
+    from ulab import scipy, numpy as np
+except ImportError:
+    import scipy
+    import numpy as np
+
+x = np.array((1,2,3))
+y = np.array((1,10,100,1000))
+result = (scipy.signal.oaconvolve(x, y))
+ref_result = np.array([1, 12, 123, 1230, 2300, 3000],dtype=np.float)
+cmp_result = []
+for p,q in zip(list(result), list(ref_result)):
+    cmp_result.append(math.isclose(p, q, rel_tol=1e-06, abs_tol=5e-04))
+print(cmp_result)
diff --git a/tests/1d/scipy/oaconvolve.py.exp b/tests/1d/scipy/oaconvolve.py.exp
new file mode 100644
index 00000000..63a3ac63
--- /dev/null
+++ b/tests/1d/scipy/oaconvolve.py.exp
@@ -0,0 +1 @@
+[True, True, True, True, True, True]