Fixes BSS algorithm for new API of numpy.linalg.solve in numpy 2.0

Author: fakufaku

CHANGELOG.rst

@@ -26,6 +26,8 @@ Bugfix
   ticks when called with ``kind="tf"``
 - Fixes an issue where the ``visibility`` attribute of the room is not
   set if there are no visible source or image source in the room. (#313)
+- Fixes issue with cast reflections delays to float32 in room.py (#353)
+- Fixes calls to ``numpy.linalg.solve`` with Numpy 2.0 API
 
 `0.7.4`_ - 2024-04-25
 ---------------------

pyroomacoustics/bss/auxiva.py

@@ -227,7 +227,7 @@ def demix(Y, X, W):
             )
 
             WV = np.matmul(W_hat, V)
-            W[:, s, :] = np.conj(np.linalg.solve(WV, eyes[:, :, s]))
+            W[:, s, :] = np.conj(np.linalg.solve(WV, eyes[:, :, [s]]))[..., 0]
 
             # normalize
             denom = np.matmul(

pyroomacoustics/bss/fastmnmf.py

@@ -175,8 +175,8 @@ def separate():
 
                 try:
                     tmp_FM = np.linalg.solve(
-                        np.matmul(Q_FMM, V_FMM), np.eye(n_chan)[None, m]
-                    )
+                        np.matmul(Q_FMM, V_FMM), np.eye(n_chan)[None, :, [m]]
+                    )[..., 0]
                 except np.linalg.LinAlgError:
                     # If Gaussian elimination fails due to a singlular matrix, we
                     # switch to the pseudo-inverse solution

pyroomacoustics/bss/fastmnmf2.py

@@ -165,8 +165,8 @@ def separate():
                     np.einsum("ftij, ft -> fij", XX_FTMM, 1 / Y_FTM[..., m]) / n_frames
                 )
                 tmp_FM = np.linalg.solve(
-                    np.matmul(Q_FMM, V_FMM), np.eye(n_chan)[None, m]
-                )
+                    np.matmul(Q_FMM, V_FMM), np.eye(n_chan)[None, :, [m]]
+                )[..., 0]
                 Q_FMM[:, m] = (
                     tmp_FM
                     / np.sqrt(

pyroomacoustics/bss/ilrma.py

@@ -159,7 +159,7 @@ def demix(Y, X, W):
             C = np.matmul((X * iR[s, :, None, :]), np.conj(X.swapaxes(1, 2))) / n_frames
 
             WV = np.matmul(W, C)
-            W[:, s, :] = np.conj(np.linalg.solve(WV, eyes[:, :, s]))
+            W[:, s, :] = np.conj(np.linalg.solve(WV, eyes[:, :, [s]]))[..., 0]
 
             # normalize
             denom = np.matmul(

pyroomacoustics/bss/sparseauxiva.py

@@ -148,7 +148,7 @@ def demixsparse(Y, X, S, W):
             W_H = np.conj(np.swapaxes(W, 1, 2))
             WV = np.matmul(W_H, V[:, s, :, :])
             rhs = I[None, :, s][[0] * WV.shape[0], :]
-            W[:, :, s] = np.linalg.solve(WV, rhs)
+            W[:, :, s] = np.linalg.solve(WV, rhs[..., None])[..., 0]
 
             # normalize
             P1 = np.conj(W[:, :, s])